School suicide analysis 2021 update.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check that you're in python 2\n",
    "import sys\n",
    "assert sys.version[0] == '2'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import zipfile\n",
    "import StringIO\n",
    "import requests\n",
    "import statsmodels.api as sm\n",
    "import patsy\n",
    "import numpy as np\n",
    "from IPython.display import clear_output\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "seaborn.set_style('white')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only set this to true if you want to redownload all the 10s of gigabytes of raw data\n",
    "REDOWNLOAD_RAW = False\n",
    "if REDOWNLOAD_RAW:\n",
    "    raw_input(\"Are you sure you want to redownload everything?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "SAVE_FIGS = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "COLORS = [\"#9b59b6\", \"#3498db\", \"#95a5a6\", \"#e74c3c\", \"#34495e\", \"#2ecc71\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "START_YEAR = 1979\n",
    "END_YEAR = 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Death data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "BASE_URL = \"https://www.nber.org/mortality/{0}/mort{0}.csv.zip\"\n",
    "URL_2019 = \"http://data.nber.org/mortality/2019/nber_output/mortality_2019_nber_us.csv\"  # changed for 2019...\n",
    "DOWNLOAD_DEST_BASE = 'nchs_mcd_rawdata'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# If raw data folder doesn't exist, create it\n",
    "if not os.path.exists(DOWNLOAD_DEST_BASE):\n",
    "    print 'Making \"{0}\" directory...'.format(DOWNLOAD_DEST_BASE)\n",
    "    os.makedirs('{0}'.format(DOWNLOAD_DEST_BASE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def download_raw_data_by_year(year, base_url=BASE_URL, download_dest_base=DOWNLOAD_DEST_BASE):\n",
    "    \"\"\"\n",
    "    See documentation stuff here:\n",
    "    https://www.nber.org/data/vital-statistics-mortality-data-multiple-cause-of-death.html\n",
    "    \"\"\"\n",
    "    year = str(year)\n",
    "    \n",
    "    # Check if already exists\n",
    "    if os.path.exists(os.path.join(download_dest_base, '{0}.csv'.format(year))):\n",
    "        if not REDOWNLOAD_RAW:\n",
    "            print \"Already have raw mortality data for {0}\".format(year)\n",
    "            return\n",
    "        else:\n",
    "            print \"Redownloading raw mortality data for {0}!!!!!!!!!!!!!!!!!!!!!!!\".format(year)\n",
    "    \n",
    "    # Specify file locations\n",
    "    url = base_url.format(year)\n",
    "    download_dest = os.path.join(download_dest_base, year)\n",
    "\n",
    "    # Download data\n",
    "    r = requests.get(url, stream=True)\n",
    "    with zipfile.ZipFile(StringIO.StringIO(r.content)) as zf:\n",
    "        zf.extractall(download_dest)\n",
    "\n",
    "    # Move CSV to main directory and delete empty folder\n",
    "    # nber filenames changed?\n",
    "    try:\n",
    "        current_filename = os.path.join(download_dest, 'mort{0}.csv'.format(year))\n",
    "        new_filename = os.path.join(download_dest_base, '{0}.csv'.format(year))\n",
    "        os.rename(current_filename, new_filename)\n",
    "        os.rmdir(os.path.join(download_dest_base, year))\n",
    "    except:\n",
    "        current_filename = os.path.join(download_dest, 'Mort{0}US.PubUse.csv'.format(year))\n",
    "        new_filename = os.path.join(download_dest_base, '{0}.csv'.format(year))\n",
    "        os.rename(current_filename, new_filename)\n",
    "        os.rmdir(os.path.join(download_dest_base, year))\n",
    "    \n",
    "    print \"Downloaded raw mortality data for {0}\".format(year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def download_raw_data_2019(url=URL_2019, download_dest_base=DOWNLOAD_DEST_BASE):\n",
    "    df = pd.read_csv(url)\n",
    "    download_dest = os.path.join(download_dest_base, '2019.csv')\n",
    "    df.to_csv(download_dest)\n",
    "    \n",
    "    print \"Downloaded raw mortality data for 2019\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def download_raw_data(start_year=START_YEAR, end_year=END_YEAR, \n",
    "                      base_url=BASE_URL, url_2019=URL_2019, download_dest_base=DOWNLOAD_DEST_BASE):\n",
    "    actual_end_year = min(END_YEAR, 2018)\n",
    "    years = range(start_year, actual_end_year+1)\n",
    "    \n",
    "    for year in years:\n",
    "        download_raw_data_by_year(year, base_url, download_dest_base)\n",
    "    \n",
    "    if actual_end_year < 2019:\n",
    "        download_raw_data_2019(url_2019, download_dest_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Already have raw mortality data for 1979\n",
      "Already have raw mortality data for 1980\n",
      "Already have raw mortality data for 1981\n",
      "Already have raw mortality data for 1982\n",
      "Already have raw mortality data for 1983\n",
      "Already have raw mortality data for 1984\n",
      "Already have raw mortality data for 1985\n",
      "Already have raw mortality data for 1986\n",
      "Already have raw mortality data for 1987\n",
      "Already have raw mortality data for 1988\n",
      "Already have raw mortality data for 1989\n",
      "Already have raw mortality data for 1990\n",
      "Already have raw mortality data for 1991\n",
      "Already have raw mortality data for 1992\n",
      "Already have raw mortality data for 1993\n",
      "Already have raw mortality data for 1994\n",
      "Already have raw mortality data for 1995\n",
      "Already have raw mortality data for 1996\n",
      "Already have raw mortality data for 1997\n",
      "Already have raw mortality data for 1998\n",
      "Already have raw mortality data for 1999\n",
      "Already have raw mortality data for 2000\n",
      "Already have raw mortality data for 2001\n",
      "Already have raw mortality data for 2002\n",
      "Already have raw mortality data for 2003\n",
      "Already have raw mortality data for 2004\n",
      "Already have raw mortality data for 2005\n",
      "Already have raw mortality data for 2006\n",
      "Already have raw mortality data for 2007\n",
      "Already have raw mortality data for 2008\n",
      "Already have raw mortality data for 2009\n",
      "Already have raw mortality data for 2010\n",
      "Already have raw mortality data for 2011\n",
      "Already have raw mortality data for 2012\n",
      "Already have raw mortality data for 2013\n",
      "Already have raw mortality data for 2014\n",
      "Already have raw mortality data for 2015\n",
      "Already have raw mortality data for 2016\n",
      "Already have raw mortality data for 2017\n",
      "Already have raw mortality data for 2018\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\basil\\Anaconda3\\envs\\py2\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2878: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloaded raw mortality data for 2019\n"
     ]
    }
   ],
   "source": [
    "download_raw_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Population data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "OTHER_DATA_DEST_BASE = 'other_data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# If raw data folder doesn't exist, create it\n",
    "if not os.path.exists(OTHER_DATA_DEST_BASE):\n",
    "    print 'Making \"{0}\" directory...'.format(DOWNLOAD_DEST_BASE)\n",
    "    os.makedirs('{0}'.format(OTHER_DATA_DEST_BASE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population data loaded, no need to run the rest of this section\n"
     ]
    }
   ],
   "source": [
    "pop_filename = os.path.join(OTHER_DATA_DEST_BASE, 'pop_data.csv')\n",
    "if os.path.exists(pop_filename):\n",
    "    pop_data = pd.read_csv(pop_filename, index_col=0)\n",
    "    pop_data = pop_data[[c for c in pop_data.columns if c != 'total']]\n",
    "    pop_data.columns = [int(x) for x in pop_data.columns]\n",
    "    print \"Population data loaded, no need to run the rest of this section\"\n",
    "else:\n",
    "    print \"pop_data.csv not yet created -- load data using code below.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Use NBER data (doesn't work!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# This data only runs up through 2012, so I don't end up using it. Keeping it here to show that my population data\n",
    "# directly downloaded from Census matches what NBER has up through 2012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {
    "hidden": true
   },
   "source": [
    "NBER_POP_DATA_URL = 'https://www.nber.org/seer-pop/usstatesingleagesrace.csv.zip'"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "hidden": true
   },
   "source": [
    "def download_nber_pop_data(url=NBER_POP_DATA_URL, download_dest_base=OTHER_DATA_DEST_BASE):\n",
    "    \"\"\"\n",
    "    See documentation stuff here:\n",
    "    https://www.nber.org/data/seer_u.s._county_population_data.html\n",
    "    \"\"\"\n",
    "    \n",
    "    # Check if already exists\n",
    "    if os.path.exists(os.path.join(download_dest_base, 'nber_pop_data.csv')):\n",
    "        if not REDOWNLOAD_RAW:\n",
    "            print \"Already have NBER population data\"\n",
    "            return\n",
    "        else:\n",
    "            print \"Redownloading NBER population data\"\n",
    "    \n",
    "    # Download data\n",
    "    download_dest = os.path.join(download_dest_base, 'tmp')\n",
    "    r = requests.get(url, stream=True)\n",
    "    with zipfile.ZipFile(StringIO.StringIO(r.content)) as zf:\n",
    "        zf.extractall(download_dest)\n",
    "\n",
    "    # Move CSV to main directory and delete empty folder\n",
    "    current_filename = os.path.join(download_dest, 'usstatesingleagesrace.csv')\n",
    "    new_filename = os.path.join(download_dest_base, 'nber_pop_data.csv')\n",
    "    os.rename(current_filename, new_filename)\n",
    "    os.rmdir(download_dest)\n",
    "    \n",
    "    print \"Downloaded NBER population data\""
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "hidden": true
   },
   "source": [
    "download_nber_pop_data()\n",
    "df = pd.read_csv('other_data/nber_pop_data.csv')\n",
    "df = pd.pivot_table(df[['year', 'age', 'pop']], columns='age', index='year', values='pop', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "hidden": true
   },
   "source": [
    "for i in range(14, 25):\n",
    "    df[i].plot(label='nber')\n",
    "    pop_data[i].plot(label='me')\n",
    "    plt.legend()\n",
    "    plt.title(i)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Load by decade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 2010-2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Source url: https://www.census.gov/data/tables/time-series/demo/popest/2010s-national-detail.html\n",
    "# Description: https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2018/nc-est2018-agesex-res.pdf\n",
    "POP_DATA_URL_2010 = 'https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2019/nc-est2019-agesex-res.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def download_2010_pop_data(pop_data_url_2010=POP_DATA_URL_2010):\n",
    "    \"\"\"\n",
    "    Download population data from 2010-2018\n",
    "    \"\"\"\n",
    "    # Download\n",
    "    pop_data_2010_raw = pd.read_csv(pop_data_url_2010, index_col=None, header=0)\n",
    "    \n",
    "    # Clean\n",
    "    col_map = {\n",
    "        c: c[-4:]\n",
    "        for c in pop_data_2010_raw.columns if 'POPESTIMATE' in c\n",
    "    }\n",
    "    col_map['AGE'] = 'age'\n",
    "    col_map['SEX'] = 'sex'\n",
    "    gender_map = {\n",
    "        0: 'total',\n",
    "        1: 'male',\n",
    "        2: 'female',\n",
    "    }\n",
    "\n",
    "    pop_data_2010 = pop_data_2010_raw[col_map].rename(columns=col_map)\n",
    "    pop_data_2010['sex'] = pop_data_2010['sex'].replace(gender_map)\n",
    "    pop_data_2010['age'] = pop_data_2010['age'].replace(999, 'total')\n",
    "\n",
    "    pop_data_2010 = (\n",
    "        pop_data_2010\n",
    "        .set_index(['sex', 'age'])\n",
    "        .T\n",
    "        .sort_index()\n",
    "    )\n",
    "    \n",
    "    # Return\n",
    "    return pop_data_2010['total'], pop_data_2010['male'], pop_data_2010['female']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def load_pop_data_2010(pop_data_url_2010=POP_DATA_URL_2010, other_data_dest_base=OTHER_DATA_DEST_BASE):\n",
    "    total_filename = os.path.join(other_data_dest_base, 'pop_data_2010_total.csv')\n",
    "    male_filename = os.path.join(other_data_dest_base, 'pop_data_2010_male.csv')\n",
    "    female_filename = os.path.join(other_data_dest_base, 'pop_data_2010_female.csv')\n",
    "    \n",
    "    if os.path.exists(total_filename):\n",
    "        print \"Loading\", total_filename\n",
    "        pop_data_2010_total = pd.read_csv(total_filename, index_col=0)\n",
    "        pop_data_2010_total.columns = [int(x) for x in pop_data_2010_total.columns if x != 'total'] + ['total']\n",
    "    else:\n",
    "        print \"Downloading 2010-2018 population data\"\n",
    "        pop_data_2010_total, males, females = download_2010_pop_data(pop_data_url_2010)\n",
    "        \n",
    "        pop_data_2010_total.to_csv(total_filename)\n",
    "        males.to_csv(male_filename)\n",
    "        females.to_csv(female_filename)\n",
    "        \n",
    "        print \"Saved\", total_filename\n",
    "        \n",
    "    return pop_data_2010_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading other_data\\pop_data_2010_total.csv\n"
     ]
    }
   ],
   "source": [
    "pop_data_2010 = load_pop_data_2010()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sanity check\n",
    "(pop_data_2010[26]*100./pop_data_2010['total']).plot()\n",
    "_ = plt.title('Age 26 population')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 2000-2009"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Source url: https://www.census.gov/data/datasets/time-series/demo/popest/intercensal-2000-2010-national.html\n",
    "POP_DATA_URL_2000 = 'https://www2.census.gov/programs-surveys/popest/datasets/2000-2010/intercensal/national/us-est00int-alldata.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pop_data_2000_raw = pd.read_csv(POP_DATA_URL_2000, index_col=None, header=0)\n",
    "\n",
    "# Estimates are available for April and July (??), I use July.\n",
    "pop_data_2000 = pop_data_2000_raw[pop_data_2000_raw['MONTH'] == 7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def download_2000_pop_data(pop_data_url_2000=POP_DATA_URL_2000, month=7):\n",
    "    \"\"\"\n",
    "    Download population data from 2000-2009\n",
    "    Estimates are available for July, and also for April only in 2000 and 2010 (??), so I use July.\n",
    "    \"\"\"   \n",
    "    # Download\n",
    "    pop_data_2000_raw = pd.read_csv(pop_data_url_2000, index_col=None, header=0)\n",
    "    \n",
    "    # Estimates are available for April and July (??), I use July.\n",
    "    pop_data_2000 = pop_data_2000_raw[pop_data_2000_raw['MONTH'] == month]\n",
    "\n",
    "    # Clean\n",
    "    def clean_pop_2000(df):\n",
    "        df = df.rename(columns={999: 'total'})\n",
    "        df.index.name = None\n",
    "        df.columns.name = None\n",
    "        return df\n",
    "\n",
    "    pop_data_2000_total = pd.pivot_table(pop_data_2000, index='YEAR', columns='AGE', values='TOT_POP')\n",
    "    pop_data_2000_male = pd.pivot_table(pop_data_2000, index='YEAR', columns='AGE', values='TOT_MALE')\n",
    "    pop_data_2000_female = pd.pivot_table(pop_data_2000, index='YEAR', columns='AGE', values='TOT_FEMALE')\n",
    "\n",
    "    pop_data_2000_total = clean_pop_2000(pop_data_2000_total)\n",
    "    pop_data_2000_male = clean_pop_2000(pop_data_2000_male)\n",
    "    pop_data_2000_female = clean_pop_2000(pop_data_2000_female)\n",
    "    \n",
    "    # Return\n",
    "    return pop_data_2000_total, pop_data_2000_male, pop_data_2000_female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def load_pop_data_2000(pop_data_url_2000=POP_DATA_URL_2000, other_data_dest_base=OTHER_DATA_DEST_BASE):\n",
    "    total_filename = os.path.join(other_data_dest_base, 'pop_data_2000_total.csv')\n",
    "    male_filename = os.path.join(other_data_dest_base, 'pop_data_2000_male.csv')\n",
    "    female_filename = os.path.join(other_data_dest_base, 'pop_data_2000_female.csv')\n",
    "    \n",
    "    if os.path.exists(total_filename):\n",
    "        print \"Loading\", total_filename\n",
    "        pop_data_2000_total = pd.read_csv(total_filename, index_col=0)\n",
    "        pop_data_2000_total.columns = [int(x) for x in pop_data_2000_total.columns if x != 'total'] + ['total']\n",
    "    else:\n",
    "        print \"Downloading 2000-2009 population data\"\n",
    "        pop_data_2000_total, males, females = download_2000_pop_data(pop_data_url_2000)\n",
    "        \n",
    "        pop_data_2000_total.to_csv(total_filename)\n",
    "        males.to_csv(male_filename)\n",
    "        females.to_csv(female_filename)\n",
    "        \n",
    "        print \"Saved\", total_filename\n",
    "        \n",
    "    return pop_data_2000_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading other_data\\pop_data_2000_total.csv\n"
     ]
    }
   ],
   "source": [
    "pop_data_2000 = load_pop_data_2000()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 1990-2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#https://www.census.gov/data/datasets/time-series/demo/popest/intercensal-1990-2000-national.html\n",
    "POP_DATA_URL_1990 = 'https://www2.census.gov/programs-surveys/popest/datasets/1990-2000/intercensal/national/us-est90int-07.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def download_1990_pop_data(pop_data_url_1990=POP_DATA_URL_1990, month=7):\n",
    "    \"\"\"\n",
    "    Download population data from 1990-1999\n",
    "    Estimates are available monthly, I use July.\n",
    "    \"\"\"   \n",
    "    # Download\n",
    "    pop_data_1990_raw = pd.read_csv(pop_data_url_1990, index_col=None, header=0, skiprows=1)\n",
    "    \n",
    "    # Clean\n",
    "    # Relabel columns\n",
    "    pop_data_1990 = pop_data_1990_raw.copy()\n",
    "    pop_data_1990.columns = ['Date', 'Age', 'Total', 'Male', 'Female']\n",
    "    # Drop bad rows\n",
    "    pop_data_1990 = pop_data_1990['Int' != pop_data_1990['Date'].str[:3]]\n",
    "    # Date\n",
    "    pop_data_1990['Date'] = pd.to_datetime(pop_data_1990['Date'])\n",
    "    pop_data_1990['Year'] = pop_data_1990['Date'].apply(lambda x: x.year)\n",
    "    pop_data_1990['Month'] = pop_data_1990['Date'].apply(lambda x: x.month)\n",
    "    # Age\n",
    "    pop_data_1990['Age'] = pop_data_1990['Age'].replace('100+', '100')\n",
    "    pop_data_1990['Age'] = pop_data_1990['Age'].replace('All Age', 'total')\n",
    "    # Estimates are available quarterly, I use April.\n",
    "    pop_data_1990 = pop_data_1990[pop_data_1990['Month'] == month]\n",
    "    \n",
    "    # Collect\n",
    "    pop_data_1990_total = pd.pivot_table(pop_data_1990, index='Year', columns='Age', values='Total')\n",
    "    pop_data_1990_male = pd.pivot_table(pop_data_1990, index='Year', columns='Age', values='Male')\n",
    "    pop_data_1990_female = pd.pivot_table(pop_data_1990, index='Year', columns='Age', values='Female')\n",
    "    \n",
    "    # Clean moar\n",
    "    def clean_pop_1990(df):\n",
    "        df.index.name = None\n",
    "        df.columns.name = None\n",
    "        df.columns = [int(x) if x != 'total' else 'total' for x in df.columns]\n",
    "        order = sorted(df.columns)\n",
    "        return df[order]\n",
    "    pop_data_1990_total = clean_pop_1990(pop_data_1990_total)\n",
    "    pop_data_1990_male = clean_pop_1990(pop_data_1990_male)\n",
    "    pop_data_1990_female = clean_pop_1990(pop_data_1990_female)\n",
    "    \n",
    "    # Return\n",
    "    return pop_data_1990_total, pop_data_1990_male, pop_data_1990_female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def load_pop_data_1990(pop_data_url_1990=POP_DATA_URL_1990, other_data_dest_base=OTHER_DATA_DEST_BASE):\n",
    "    total_filename = os.path.join(other_data_dest_base, 'pop_data_1990_total.csv')\n",
    "    male_filename = os.path.join(other_data_dest_base, 'pop_data_1990_male.csv')\n",
    "    female_filename = os.path.join(other_data_dest_base, 'pop_data_1990_female.csv')\n",
    "    \n",
    "    if os.path.exists(total_filename):\n",
    "        print \"Loading\", total_filename\n",
    "        pop_data_1990_total = pd.read_csv(total_filename, index_col=0)\n",
    "        pop_data_1990_total.columns = [int(x) for x in pop_data_1990_total.columns if x != 'total'] + ['total']\n",
    "    else:\n",
    "        print \"Downloading 1990-1999 population data\"\n",
    "        pop_data_1990_total, males, females = download_1990_pop_data(pop_data_url_1990)\n",
    "        \n",
    "        pop_data_1990_total.to_csv(total_filename)\n",
    "        males.to_csv(male_filename)\n",
    "        females.to_csv(female_filename)\n",
    "        \n",
    "        print \"Saved\", total_filename\n",
    "        \n",
    "    return pop_data_1990_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading other_data\\pop_data_1990_total.csv\n"
     ]
    }
   ],
   "source": [
    "pop_data_1990 = load_pop_data_1990()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### 1980-1990"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Source: https://www.census.gov/data/datasets/time-series/demo/popest/1980s-national.html\n",
    "# Documentation: https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/1980-1990/nat-detail-layout.txt\n",
    "POP_DATA_URLS_1980s = {\n",
    "    1980: 'https://www2.census.gov/programs-surveys/popest/datasets/1980-1990/national/asrh/e8081rqi.zip',\n",
    "    1981: 'https://www2.census.gov/programs-surveys/popest/datasets/1980-1990/national/asrh/e8182rqi.zip',\n",
    "    1982: 'https://www2.census.gov/programs-surveys/popest/datasets/1980-1990/national/asrh/e8283rqi.zip',\n",
    "    1983: 'https://www2.census.gov/programs-surveys/popest/datasets/1980-1990/national/asrh/e8384rqi.zip',\n",
    "    1984: 'https://www2.census.gov/programs-surveys/popest/datasets/1980-1990/national/asrh/e8485rqi.zip',\n",
    "    1985: 'https://www2.census.gov/programs-surveys/popest/datasets/1980-1990/national/asrh/e8586rqi.zip',\n",
    "    1986: 'https://www2.census.gov/programs-surveys/popest/datasets/1980-1990/national/asrh/e8687rqi.zip',\n",
    "    1987: 'https://www2.census.gov/programs-surveys/popest/datasets/1980-1990/national/asrh/e8788rqi.zip',\n",
    "    1988: 'https://www2.census.gov/programs-surveys/popest/datasets/1980-1990/national/asrh/e8889rqi.zip',\n",
    "    1989: 'https://www2.census.gov/programs-surveys/popest/datasets/1980-1990/national/asrh/e8990rqi.zip',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def download_1980_pop_data_by_year(year, pop_data_urls_1980s=POP_DATA_URLS_1980s, \n",
    "                                   other_data_dest_base=OTHER_DATA_DEST_BASE, month=7):\n",
    "    \"\"\"\n",
    "    Download population data for a given year in 1980-1989\n",
    "    Estimates are available quarterly, I use July.\n",
    "    \"\"\"   \n",
    "    # Download zip for the year\n",
    "    url = pop_data_urls_1980s[year]\n",
    "    r = requests.get(url, stream=True)\n",
    "    with zipfile.ZipFile(StringIO.StringIO(r.content)) as zf:\n",
    "        zf.extractall(other_data_dest_base)    \n",
    "    \n",
    "    # Read in data (stored as a string wtf)\n",
    "    year_str2 = str(year)[-2:]\n",
    "    year_after_str2 = str(year+1)[-2:]\n",
    "    raw_data_name = 'E{0}{1}RQI.TXT'.format(year_str2, year_after_str2)\n",
    "    raw_data_path = os.path.join(other_data_dest_base, raw_data_name)\n",
    "    raw_data = open(raw_data_path, 'r').readlines()\n",
    "\n",
    "    pop_data_year = {\n",
    "        'Month': {},\n",
    "        'Year': {},\n",
    "        'Age': {},\n",
    "        'Total': {},\n",
    "        'Male': {},\n",
    "        'Female': {},\n",
    "    }\n",
    "    for row_num in range(len(raw_data)):\n",
    "        row = raw_data[row_num]\n",
    "        if len(row) < 40:\n",
    "            continue\n",
    "        #pop_data_year['series'][row_num] = row[0:2]\n",
    "        pop_data_year['Month'][row_num] = int(row[2:4])\n",
    "        pop_data_year['Year'][row_num] = int('19' + row[4:6])\n",
    "        pop_data_year['Age'][row_num] = int(row[6:10])\n",
    "        #pop_data_year['blank'][row_num] = row[10:11]\n",
    "        pop_data_year['Total'][row_num] = int(row[11:21])\n",
    "        pop_data_year['Male'][row_num] = int(row[21:31])\n",
    "        pop_data_year['Female'][row_num] = int(row[31:41])\n",
    "    \n",
    "    # Clean up\n",
    "    pop_data_year = pd.DataFrame(pop_data_year)\n",
    "    os.remove(raw_data_path)\n",
    "    \n",
    "    # Clean\n",
    "    pop_data_year['Age'] = pop_data_year['Age'].replace(999, 'total')\n",
    "    pop_data_year = pop_data_year[pop_data_year['Year'] == year]\n",
    "    pop_data_year = pop_data_year[pop_data_year['Month'] == month]\n",
    "\n",
    "    \n",
    "    return pop_data_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def download_1980_pop_data(pop_data_urls_1980s=POP_DATA_URLS_1980s, \n",
    "                           other_data_dest_base=OTHER_DATA_DEST_BASE, month=7):\n",
    "    \"\"\"\n",
    "    Download population data from 1980-1989\n",
    "    Estimates are available monthly, I use July.\n",
    "    \"\"\"   \n",
    "    # Download\n",
    "    pop_data_1980_dict = {}\n",
    "    for year in range(1980, 1990):\n",
    "        pop_data_1980_dict[year] = download_1980_pop_data_by_year(year, pop_data_urls_1980s, \n",
    "                                                                  other_data_dest_base, month)\n",
    "    pop_data_1980 = pd.concat(pop_data_1980_dict.values(), axis=0)\n",
    "        \n",
    "    # Collect\n",
    "    pop_data_1980_total = pd.pivot_table(pop_data_1980, index='Year', columns='Age', values='Total')\n",
    "    pop_data_1980_male = pd.pivot_table(pop_data_1980, index='Year', columns='Age', values='Male')\n",
    "    pop_data_1980_female = pd.pivot_table(pop_data_1980, index='Year', columns='Age', values='Female')\n",
    "    \n",
    "    # Clean moar\n",
    "    def clean_pop_1980(df):\n",
    "        df.index.name = None\n",
    "        df.columns.name = None\n",
    "        df.columns = [int(x) if x != 'total' else 'total' for x in df.columns]\n",
    "        order = sorted(df.columns)\n",
    "        return df[order]\n",
    "    pop_data_1980_total = clean_pop_1980(pop_data_1980_total)\n",
    "    pop_data_1980_male = clean_pop_1980(pop_data_1980_male)\n",
    "    pop_data_1980_female = clean_pop_1980(pop_data_1980_female)\n",
    "    \n",
    "    # Return\n",
    "    return pop_data_1980_total, pop_data_1980_male, pop_data_1980_female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def load_pop_data_1980(pop_data_urls_1980s=POP_DATA_URLS_1980s, other_data_dest_base=OTHER_DATA_DEST_BASE):\n",
    "    total_filename = os.path.join(other_data_dest_base, 'pop_data_1980_total.csv')\n",
    "    male_filename = os.path.join(other_data_dest_base, 'pop_data_1980_male.csv')\n",
    "    female_filename = os.path.join(other_data_dest_base, 'pop_data_1980_female.csv')\n",
    "    \n",
    "    if os.path.exists(total_filename):\n",
    "        print \"Loading\", total_filename\n",
    "        pop_data_1980_total = pd.read_csv(total_filename, index_col=0)\n",
    "        pop_data_1980_total.columns = [int(x) for x in pop_data_1980_total.columns if x != 'total'] + ['total']\n",
    "    else:\n",
    "        print \"Downloading 1990-1999 population data\"\n",
    "        pop_data_1980_total, males, females = download_1980_pop_data(pop_data_urls_1980s, other_data_dest_base)\n",
    "        \n",
    "        pop_data_1980_total.to_csv(total_filename)\n",
    "        males.to_csv(male_filename)\n",
    "        females.to_csv(female_filename)\n",
    "        \n",
    "        print \"Saved\", total_filename\n",
    "        \n",
    "    return pop_data_1980_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading other_data\\pop_data_1980_total.csv\n"
     ]
    }
   ],
   "source": [
    "pop_data_1980 = load_pop_data_1980()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### <1980"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "#TODO\n",
    "#https://www.census.gov/data/tables/time-series/demo/popest/pre-1980-national.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Combine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Note that the \"max bucket\" varies over time, but doesn't matter for our purposes\n",
    "# See https://www.census.gov/dataviz/visualizations/055/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "dict_of_pop_data = {\n",
    "    '2010-2017': pop_data_2010,\n",
    "    '2000-2009': pop_data_2000.loc[2000:2009],\n",
    "    '1990-1999': pop_data_1990,\n",
    "    '1980-1989': pop_data_1980,\n",
    "    #'<1980': None,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pop_data = pd.concat(dict_of_pop_data.values(), axis=0)\n",
    "pop_data = pop_data.fillna(0)\n",
    "pop_data = pop_data.sort_index()\n",
    "\n",
    "clear_output() # stupid warning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fb7f148>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sanity check\n",
    "pop_data['total'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Save\n",
    "pop_data.to_csv(os.path.join(OTHER_DATA_DEST_BASE, 'pop_data.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Get just suicide data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "CLEAN_DATA_BASE = 'cleaned_data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "if not os.path.exists(CLEAN_DATA_BASE):\n",
    "    os.makedirs(CLEAN_DATA_BASE)\n",
    "    print \"Created\", CLEAN_DATA_BASE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# See Hansen and Lang (2011), \"Data\" section; or individual documentation files linked from NBER\n",
    "DEATH_VAR_MAP = {}\n",
    "SUICIDE_VAL_MAP = {}\n",
    "#for year in range(1968, 1979):\n",
    "    # ICDA-8\n",
    "    # https://www.nber.org/mortality/www.cdc.gov/nchs/data/dvs/dt78icd8.pdf\n",
    "    #DEATH_VAR_MAP[year] = 'ucr34'\n",
    "    #SUICIDE_VAL_MAP[year] = 350\n",
    "for year in range(1979, 1999):\n",
    "    # ICDA-9\n",
    "    # https://www.nber.org/mortality/1998/desc_mort1998.txt\n",
    "    DEATH_VAR_MAP[year] = 'ucr34'\n",
    "    SUICIDE_VAL_MAP[year] = 350\n",
    "for year in range(1999, 2020):\n",
    "    # ICDA-10\n",
    "    # https://www.nber.org/mortality/1999/desc_mort1999.txt\n",
    "    DEATH_VAR_MAP[year] = 'ucr39'\n",
    "    SUICIDE_VAL_MAP[year] = 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def get_suicide_data(death_var_map=DEATH_VAR_MAP, suicide_val_map=SUICIDE_VAL_MAP,\n",
    "                     start_year=START_YEAR, end_year=END_YEAR,\n",
    "                     raw_data_loc_base=DOWNLOAD_DEST_BASE, suicide_data_loc_base=CLEAN_DATA_BASE):\n",
    "    for year in reversed(range(start_year, end_year+1)):\n",
    "        # Check if already exists\n",
    "        new_filename = os.path.join(suicide_data_loc_base, '{0}.csv'.format(year))\n",
    "        if os.path.exists(new_filename):\n",
    "            print new_filename, \"already exists\"\n",
    "            continue\n",
    "\n",
    "        # Set appropriate variables\n",
    "        death_var = death_var_map[int(year)]\n",
    "        suicide_val = suicide_val_map[int(year)]\n",
    "        \n",
    "        # Get data\n",
    "        get_suicide_data_by_year(death_var, suicide_val, year, raw_data_loc_base, suicide_data_loc_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def get_suicide_data_by_year(death_var, suicide_val, year,\n",
    "                             raw_data_loc_base=DOWNLOAD_DEST_BASE, suicide_data_loc_base=CLEAN_DATA_BASE):\n",
    "    # Check if already exists\n",
    "    new_filename = os.path.join(suicide_data_loc_base, '{0}.csv'.format(year))\n",
    "    if os.path.exists(new_filename):\n",
    "        print new_filename, \"already exists\"\n",
    "        return\n",
    "\n",
    "    # Load data\n",
    "    print \"Loading\", year\n",
    "    filename = os.path.join(raw_data_loc_base, '{0}.csv'.format(year))\n",
    "    data = pd.read_csv(filename)\n",
    "\n",
    "    # Filter to just suicides\n",
    "    suicide_data = data[data[death_var] == suicide_val]\n",
    "\n",
    "    # Extract age\n",
    "    if (year <= 2002) & (year >= 1979):    # this might work for prior to 1979 as well, but haven't checked codebooks\n",
    "        # See \"Detail Age\": https://www.nber.org/mortality/2002/mort2002.pdf\n",
    "        # above 99 label as 100\n",
    "        suicide_data.loc[(suicide_data['age'] > 100) & (suicide_data['age'] < 200), 'age'] = 100\n",
    "        # months/weeks/days/hours/minutes report as 0\n",
    "        suicide_data.loc[(suicide_data['age'] > 200) & (suicide_data['age'] < 700), 'age'] = 0\n",
    "        # drop unknowns\n",
    "        clean_suicide_data = suicide_data[suicide_data['age'] != 999]\n",
    "    elif year >= 2003:\n",
    "        # See \"Detail Age\": https://www.nber.org/mortality/2003/mort2003.pdf\n",
    "        age_in_years = (suicide_data['age'] > 1000) & (suicide_data['age'] < 2000)\n",
    "        age_zero = (suicide_data['age'] > 2000) & (suicide_data['age'] < 7000)\n",
    "        not_missing = suicide_data['age'] != 9999\n",
    "        # get age for 1+\n",
    "        suicide_data.loc[age_in_years, 'age'] -= 1000\n",
    "        # months/weeks/days/hours/minutes report as 0\n",
    "        suicide_data.loc[age_zero, 'age'] = 0\n",
    "        # above 99 label as 100\n",
    "        suicide_data.loc[(suicide_data['age'] > 100) & not_missing, 'age'] = 100\n",
    "        # drop unknowns\n",
    "        clean_suicide_data = suicide_data.loc[not_missing]\n",
    "\n",
    "    # Drop irrelevant vars\n",
    "    final_suicide_data = clean_suicide_data[['sex', 'age', 'monthdth']].copy()\n",
    "    final_suicide_data = final_suicide_data.rename(columns={'monthdth': 'month'})\n",
    "    final_suicide_data['year'] = year\n",
    "\n",
    "    # Get sex\n",
    "    # TODO: ...confirm\n",
    "    # final_suicide_data['sex'].replace(1, 'M').replace(2, 'F')\n",
    "\n",
    "    # Save data\n",
    "    final_suicide_data.to_csv(new_filename)\n",
    "    print \"Saved\", new_filename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading 2019\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\basil\\Anaconda3\\envs\\py2\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2878: DtypeWarning: Columns (8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "C:\\Users\\basil\\Anaconda3\\envs\\py2\\lib\\site-packages\\pandas\\core\\indexing.py:543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved cleaned_data\\2019.csv\n",
      "Loading 2018\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\basil\\Anaconda3\\envs\\py2\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2878: DtypeWarning: Columns (66,69,72,100,101,102,103) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved cleaned_data\\2018.csv\n",
      "cleaned_data\\2017.csv already exists\n",
      "cleaned_data\\2016.csv already exists\n",
      "cleaned_data\\2015.csv already exists\n",
      "cleaned_data\\2014.csv already exists\n",
      "cleaned_data\\2013.csv already exists\n",
      "cleaned_data\\2012.csv already exists\n",
      "cleaned_data\\2011.csv already exists\n",
      "cleaned_data\\2010.csv already exists\n",
      "cleaned_data\\2009.csv already exists\n",
      "cleaned_data\\2008.csv already exists\n",
      "cleaned_data\\2007.csv already exists\n",
      "cleaned_data\\2006.csv already exists\n",
      "cleaned_data\\2005.csv already exists\n",
      "cleaned_data\\2004.csv already exists\n",
      "cleaned_data\\2003.csv already exists\n",
      "cleaned_data\\2002.csv already exists\n",
      "cleaned_data\\2001.csv already exists\n",
      "cleaned_data\\2000.csv already exists\n",
      "cleaned_data\\1999.csv already exists\n",
      "cleaned_data\\1998.csv already exists\n",
      "cleaned_data\\1997.csv already exists\n",
      "cleaned_data\\1996.csv already exists\n",
      "cleaned_data\\1995.csv already exists\n",
      "cleaned_data\\1994.csv already exists\n",
      "cleaned_data\\1993.csv already exists\n",
      "cleaned_data\\1992.csv already exists\n",
      "cleaned_data\\1991.csv already exists\n",
      "cleaned_data\\1990.csv already exists\n",
      "cleaned_data\\1989.csv already exists\n",
      "cleaned_data\\1988.csv already exists\n",
      "cleaned_data\\1987.csv already exists\n",
      "cleaned_data\\1986.csv already exists\n",
      "cleaned_data\\1985.csv already exists\n",
      "cleaned_data\\1984.csv already exists\n",
      "cleaned_data\\1983.csv already exists\n",
      "cleaned_data\\1982.csv already exists\n",
      "cleaned_data\\1981.csv already exists\n",
      "cleaned_data\\1980.csv already exists\n",
      "cleaned_data\\1979.csv already exists\n"
     ]
    }
   ],
   "source": [
    "get_suicide_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "hidden": true
   },
   "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>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1309381</th>\n",
       "      <td>F</td>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846905</th>\n",
       "      <td>M</td>\n",
       "      <td>40</td>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62353</th>\n",
       "      <td>M</td>\n",
       "      <td>76</td>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20006</th>\n",
       "      <td>M</td>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2316075</th>\n",
       "      <td>M</td>\n",
       "      <td>81</td>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sex  age  month  year\n",
       "1309381   F   54      4  2019\n",
       "846905    M   40     12  2019\n",
       "62353     M   76      2  2019\n",
       "20006     M   35      4  2019\n",
       "2316075   M   81      5  2019"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example\n",
    "df = pd.read_csv('cleaned_data/2019.csv', index_col=0)\n",
    "df.sample(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "hidden": true
   },
   "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>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1842578</th>\n",
       "      <td>F</td>\n",
       "      <td>66</td>\n",
       "      <td>10</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938834</th>\n",
       "      <td>F</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1817012</th>\n",
       "      <td>M</td>\n",
       "      <td>61</td>\n",
       "      <td>7</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288169</th>\n",
       "      <td>M</td>\n",
       "      <td>51</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713905</th>\n",
       "      <td>M</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sex  age  month  year\n",
       "1842578   F   66     10  2017\n",
       "938834    F   32     11  2017\n",
       "1817012   M   61      7  2017\n",
       "2288169   M   51      5  2017\n",
       "713905    M   51      1  2017"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example\n",
    "df = pd.read_csv('cleaned_data/2017.csv', index_col=0)\n",
    "df.sample(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "hidden": true
   },
   "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>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1020162</th>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>9</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624118</th>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>8</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975566</th>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1775005</th>\n",
       "      <td>1</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357564</th>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>9</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sex  age  month  year\n",
       "1020162    1   49      9  1992\n",
       "1624118    1   59      8  1992\n",
       "1975566    1   27      8  1992\n",
       "1775005    1   75     12  1992\n",
       "357564     1   38      9  1992"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example\n",
    "df = pd.read_csv('cleaned_data/1992.csv', index_col=0)\n",
    "df.sample(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Get deaths of despair "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "hidden": true
   },
   "source": [
    "ICD_VAR_MAP = {}\n",
    "# ICD 9 -- DOUBLE CHECK START AND END DATE, RAFKIN CONFLICTS\n",
    "for i in range(1979, 2000):\n",
    "    ICD_VAR_MAP[i] = 'ucod'\n",
    "# ICD 10 -- DOUBLE CHECK START AND END DATE\n",
    "for i in range(2000, 2018):\n",
    "    ICD_VAR_MAP[i] = 'ucod'"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "hidden": true
   },
   "source": [
    "# ICD 9\n",
    "ICD_9_MAP = {}\n",
    "for i in range(1400, 2090):\n",
    "    ICD_9_MAP[i] = 'Cancer'\n",
    "for i in range(3900, 4300):\n",
    "    ICD_9_MAP[i] = 'Heart disease'\n",
    "for i in range(8000, 10000):\n",
    "    if i not in range(570, 580) + range(8500, 8610) + range(9500, 9600) + range(9800, 9810):\n",
    "        ICD_9_MAP[i] = 'Injuries'\n",
    "# Despair\n",
    "for i in range(570, 580):\n",
    "    ICD_9_MAP[i] = 'Despair'\n",
    "for i in range(8500, 8610):\n",
    "    ICD_9_MAP[i] = 'Despair'\n",
    "for i in range(9500, 9600):\n",
    "    ICD_9_MAP[i] = 'Despair'\n",
    "for i in range(9800, 9810):\n",
    "    ICD_9_MAP[i] = 'Despair'\n",
    "# Other\n",
    "for i in range(1, 10001):\n",
    "    if i not in ICD_9_MAP:\n",
    "        ICD_9_MAP[i] = 'Other diseases'"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "hidden": true
   },
   "source": [
    "# Source:\n",
    "# Deaths of despair: https://www.brookings.edu/wp-content/uploads/2017/03/casedeaton_sp17_dataappendix.pdf\n",
    "# \"Injuries\" from Novosad-Rafkin fwiw\n",
    "# ICD 10\n",
    "ICD_10_MAP = {}\n",
    "# Cancer\n",
    "for i in range(0, 980):\n",
    "    ICD_10_MAP['C' + str(i)] = 'Cancer'\n",
    "    ICD_10_MAP['C0' + str(i)] = 'Cancer'\n",
    "    ICD_10_MAP['C00' + str(i)] = 'Cancer'\n",
    "# Heart disease -- todo: not sure if I'm actually coding this correctly with all the silly zeros...\n",
    "for i in range(0, 100):\n",
    "    ICD_10_MAP['I' + str(i)] = 'Heart disease'\n",
    "    ICD_10_MAP['I0' + str(i)] = 'Heart disease'\n",
    "    ICD_10_MAP['I00' + str(i)] = 'Heart disease'\n",
    "for i in range(110, 120):\n",
    "    ICD_10_MAP['I' + str(i)] = 'Heart disease'\n",
    "for i in range(130, 140):\n",
    "    ICD_10_MAP['I' + str(i)] = 'Heart disease'\n",
    "for i in range(200, 520):\n",
    "    ICD_10_MAP['I' + str(i)] = 'Heart disease'\n",
    "# Injuries\n",
    "for i in range(0, 1001):\n",
    "    for letter in ['V', 'W', 'X', 'Y']:\n",
    "        if letter + str(i) not in ICD_10_MAP:\n",
    "            ICD_10_MAP[letter + str(i)] = 'Injuries'\n",
    "        if letter + '0' + str(i) not in ICD_10_MAP:\n",
    "            ICD_10_MAP[letter + '0' + str(i)] = 'Injuries'\n",
    "        if letter + '00' + str(i) not in ICD_10_MAP:\n",
    "            ICD_10_MAP[letter + '00' + str(i)] = 'Injuries'\n",
    "# Deaths of despair\n",
    "# Poisoning\n",
    "for i in range(400, 460):\n",
    "    ICD_10_MAP['X' + str(i)] = 'Poisoning'\n",
    "for i in range(100, 150):\n",
    "    ICD_10_MAP['Y' + str(i)] = 'Poisoning'\n",
    "for i in range(450, 460):\n",
    "    ICD_10_MAP['Y' + str(i)] = 'Poisoning'\n",
    "for i in range(470, 480):\n",
    "    ICD_10_MAP['Y' + str(i)] = 'Poisoning'\n",
    "for i in range(490, 500):\n",
    "    ICD_10_MAP['Y' + str(i)] = 'Poisoning'\n",
    "# Suicide\n",
    "for i in range(600, 850):\n",
    "    ICD_10_MAP['X' + str(i)] = 'Suicide'\n",
    "for i in range(870, 880):\n",
    "    ICD_10_MAP['Y' + str(i)] = 'Suicide'\n",
    "# Liver disease\n",
    "for i in range(700, 710):\n",
    "    ICD_10_MAP['K' + str(i)] = 'Liver disease'\n",
    "for i in range(730, 750):\n",
    "    ICD_10_MAP['K' + str(i)] = 'Liver disease'"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "hidden": true
   },
   "source": [
    "test_filename1992 = os.path.join(DOWNLOAD_DEST_BASE, '1992.csv')\n",
    "test_data1992 =  pd.read_csv(test_filename1992)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "hidden": true
   },
   "source": [
    "test_filename2015 = os.path.join(DOWNLOAD_DEST_BASE, '2015.csv')\n",
    "test_data2015 = pd.read_csv(test_filename2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Group by age and compute suicide rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Stack suicide data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "START_YEAR = 1980\n",
    "# 1980 not 1979 until we get population data for then"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def stack_suicide_data(start_year=START_YEAR, end_year=END_YEAR, suicide_data_loc_base=CLEAN_DATA_BASE):\n",
    "    data = {}\n",
    "    for year in range(start_year, end_year+1):\n",
    "        filename = os.path.join(suicide_data_loc_base, '{0}.csv'.format(year))\n",
    "        data[year] = pd.read_csv(filename, index_col=0)\n",
    "        \n",
    "    stacked_data = pd.concat(data.values(), axis=0)\n",
    "    return stacked_data, data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "suicide_data, tmp = stack_suicide_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "hidden": true
   },
   "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>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1842291</th>\n",
       "      <td>M</td>\n",
       "      <td>53</td>\n",
       "      <td>9</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487289</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>11</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1097445</th>\n",
       "      <td>M</td>\n",
       "      <td>55</td>\n",
       "      <td>10</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923389</th>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503697</th>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>11</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sex  age  month  year\n",
       "1842291   M   53      9  2018\n",
       "487289    1   60     11  1989\n",
       "1097445   M   55     10  2019\n",
       "1923389   1   23     10  1986\n",
       "503697    1   61     11  1991"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_data.sample(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x3a762c8>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# jfc\n",
    "suicide_data['year'].value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Group suicide data by age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "AGE_GROUPS = {\n",
    "    '<14': range(0, 14),\n",
    "    '14-18': range(14, 19),\n",
    "    '19-25': range(19, 26),\n",
    "    '>25': range(26, 101)\n",
    "}\n",
    "\n",
    "# alternative 1:\n",
    "#AGE_GROUPS = {\n",
    "#    '<18': range(0, 18),\n",
    "#    '>=18': range(18, 101),\n",
    "#}\n",
    "\n",
    "# alternative 2:\n",
    "#AGE_GROUPS = {\n",
    "#    '<18': range(0, 18),\n",
    "#    '18-25': range(18, 26),\n",
    "#    '26+': range(26, 101)\n",
    "#}\n",
    "\n",
    "# alternative 3:\n",
    "#AGE_GROUPS = {\n",
    "#    '<6': range(6),\n",
    "#    '07': [7],\n",
    "#    '08': [8],\n",
    "#    '09': [9],\n",
    "#    '>60': range(61, 101)\n",
    "#}\n",
    "#for i in range(10, 61):\n",
    "#    AGE_GROUPS[str(i)] = [i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def get_age_order(age_groups):\n",
    "    first_val = [k for k in age_groups if '<' in k]\n",
    "    last_val = [k for k in age_groups if '>' in k]\n",
    "    other = [k for k in age_groups if k not in first_val and k not in last_val]\n",
    "    other = sorted(other)\n",
    "    return first_val + other + last_val\n",
    "# should output ['<15', '15-18', '19-25', '>25']\n",
    "GROUP_ORDER = get_age_order(AGE_GROUPS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def get_age_groups_map(age_groups):\n",
    "    return {\n",
    "        v: k\n",
    "        for k in age_groups\n",
    "        for v in age_groups[k]\n",
    "    }\n",
    "AGE_GROUPS_MAP = get_age_groups_map(AGE_GROUPS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "suicide_data['age_group'] = suicide_data['age'].map(AGE_GROUPS_MAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x66a5d608>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# :(((((((((((((\n",
    "tmp = pd.pivot_table(data=suicide_data[['age_group', 'year']], index='year', columns='age_group', aggfunc=len)\n",
    "(tmp/tmp.loc[1980]).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "suicide_counts_by_age_group_by_monthyear = (\n",
    "    suicide_data[['year', 'month', 'age_group']]\n",
    "    .groupby(['year', 'month', 'age_group'])\n",
    "    .apply(len)\n",
    "    .unstack()\n",
    "    .fillna(0)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "hidden": true
   },
   "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>age_group</th>\n",
       "      <th>14-18</th>\n",
       "      <th>19-25</th>\n",
       "      <th>&lt;14</th>\n",
       "      <th>&gt;25</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1980</th>\n",
       "      <th>1</th>\n",
       "      <td>114.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102.0</td>\n",
       "      <td>376.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>112.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1772.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>86.0</td>\n",
       "      <td>391.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1848.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>103.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1846.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "age_group   14-18  19-25   <14     >25\n",
       "year month                            \n",
       "1980 1      114.0  377.0   7.0  1650.0\n",
       "     2      102.0  376.0   8.0  1570.0\n",
       "     3      112.0  401.0  10.0  1772.0\n",
       "     4       86.0  391.0   9.0  1848.0\n",
       "     5      103.0  367.0   4.0  1846.0"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview\n",
    "suicide_counts_by_age_group_by_monthyear.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Group population data by age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pop_by_group = pd.DataFrame()\n",
    "for group_k, group_v in AGE_GROUPS.iteritems():\n",
    "    pop_by_group[group_k] = pop_data[group_v].sum(1)\n",
    "pop_by_group = pop_by_group[GROUP_ORDER]\n",
    "pop_by_group.index.name = 'year'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "blank_df = pd.DataFrame(index=suicide_counts_by_age_group_by_monthyear.index)\n",
    "pop_by_group_by_monthyear = blank_df.join(pop_by_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "hidden": true
   },
   "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></th>\n",
       "      <th>&lt;14</th>\n",
       "      <th>14-18</th>\n",
       "      <th>19-25</th>\n",
       "      <th>&gt;25</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1980</th>\n",
       "      <th>1</th>\n",
       "      <td>47540375.0</td>\n",
       "      <td>20372913.0</td>\n",
       "      <td>30006209.0</td>\n",
       "      <td>129305184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47540375.0</td>\n",
       "      <td>20372913.0</td>\n",
       "      <td>30006209.0</td>\n",
       "      <td>129305184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47540375.0</td>\n",
       "      <td>20372913.0</td>\n",
       "      <td>30006209.0</td>\n",
       "      <td>129305184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47540375.0</td>\n",
       "      <td>20372913.0</td>\n",
       "      <td>30006209.0</td>\n",
       "      <td>129305184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>47540375.0</td>\n",
       "      <td>20372913.0</td>\n",
       "      <td>30006209.0</td>\n",
       "      <td>129305184.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   <14       14-18       19-25          >25\n",
       "year month                                                 \n",
       "1980 1      47540375.0  20372913.0  30006209.0  129305184.0\n",
       "     2      47540375.0  20372913.0  30006209.0  129305184.0\n",
       "     3      47540375.0  20372913.0  30006209.0  129305184.0\n",
       "     4      47540375.0  20372913.0  30006209.0  129305184.0\n",
       "     5      47540375.0  20372913.0  30006209.0  129305184.0"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview\n",
    "pop_by_group_by_monthyear.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f27c848>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# preview\n",
    "tmp_index = [pd.datetime(x[0], x[1], 1) for x in pop_by_group_by_monthyear.index]\n",
    "tmp = pop_by_group_by_monthyear.copy()\n",
    "tmp.index = tmp_index\n",
    "tmp[['<14']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# population share trends\n",
    "age_group_shares = pop_by_group.divide(pop_by_group.sum(1), axis=0)\n",
    "\n",
    "age_group_shares.plot(figsize=(12, 8), style='-*', color=COLORS)\n",
    "_ = plt.title('Fraction of population by age group, {0}-{1}'.format(START_YEAR, END_YEAR), fontsize=16, weight='heavy')\n",
    "_ = plt.xticks(fontsize=14)\n",
    "_ = plt.xlabel('year', fontsize=14)\n",
    "_ = plt.yticks(fontsize=14)\n",
    "_ = plt.legend(fontsize=14)\n",
    "plt.savefig('figures/age_shares.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# population share relative to 1980\n",
    "(age_group_shares/age_group_shares.loc[1980]).plot(figsize=(12, 8), style='-*', color=COLORS)\n",
    "_ = plt.title('Fraction of population by age group, relative to 1980', fontsize=16, weight='heavy')\n",
    "_ = plt.xticks(fontsize=14)\n",
    "_ = plt.xlabel('year', fontsize=14)\n",
    "_ = plt.yticks(fontsize=14)\n",
    "_ = plt.legend(fontsize=14)\n",
    "plt.savefig('figures/age_shares_relative_to_1980.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbb037808>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_group_shares['14-18'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Normalize suicide data by population data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Get data by month-year\n",
    "suicide_rate_by_monthyear = suicide_counts_by_age_group_by_monthyear/pop_by_group_by_monthyear\n",
    "\n",
    "raw_suicide_rate_per100k_annualized_by_monthyear = suicide_rate_by_monthyear[GROUP_ORDER]*12*100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "new_index = [\n",
    "    pd.datetime(x[0], x[1], 1) \n",
    "    for x \n",
    "    in raw_suicide_rate_per100k_annualized_by_monthyear.index\n",
    "]\n",
    "suicide_rate_per100k_annualized_by_monthyear = raw_suicide_rate_per100k_annualized_by_monthyear.copy()\n",
    "suicide_rate_per100k_annualized_by_monthyear.index = new_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9a5ebac8>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAD7CAYAAACVMATUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi41LCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvSM8oowAAIABJREFUeJzsfXfcHVWd/jMzt701vUBCEkoYCIEACZBAKNJE0AUFWUWxgS623UXX/eG6ithAF9S1IKCwurZd14W10ESqJBQBhVAylBBIQnp5+3vvnfL7Y+Z75syZM3PnlrckOc/nw4e8d/rMme885/k2zfM8KCgoKCjsGdDH+gQUFBQUFFoHZdQVFBQU9iAoo66goKCwB0EZdQUFBYU9CMqoKygoKOxBUEZdQUFBYQ+CMuoKCgoKexCUUVdQUFDYg6CMuoKCgsIehNxoH/C4447zZs2aNdqHVVBQUNit8dxzz22zLGtarfVG3ajPmjULt95662gfVkFBQWG3hmmar2VZT8kvCgoKCnsQlFFXUFBQ2IOgjLqCgoLCHgRl1BUUFBT2ICijrqCgoLAHQRl1BQUFhT0IyqgrKCgo7EGoGadumqYO4HoAiwCUAVxqWdbL3PL3APg0AAfALZZl/WCEzlVBQUFhj8TOgQoeWbMdbzKn445VG/GOo2dB07SG9pUl+eg8ACXLspaZprkUwHUAzuWWXwvgMAD9AJ43TfO/LMva2dDZKCgoKOyFuOxnT+KxV3fgzYfNwN3PbcY+E0o4/qCpDe0ri/yyHMBdAGBZ1qMAlgjLnwEwAUAJgAZAdbJWUFBQqAPrdgwCAF7fMQQA6C/bDe8ri1HvBtDD/e2Ypskz/GcBPAngOQC/tyxrV8Nno6CgoLAXw/N8Tqw3KL0A2Yx6L4AufhvLsmwAME3zCADnANgfwDwA003TfGfDZ6OgoKCggCZseiajvgLA2QAQaOqruGU9AIYADFmW5QDYAmBS46ejoKCgsPeBNOuAqI84U78NwLBpmisBfAvA5aZpXmSa5kcsy3oNwI0AHjZN82EAEwH8uOGzUVBQUNhD8YfnNmGo4uChF7di50AFALCldxiPvLKdrWO7rv+PJph6zegXy7JcAJcJP6/mlt8A4IbGT0FBQUFhz8aq9T34yE+fxNsW7YvfPf0GFs+dhP/96PE47/sr8EbPMPaZUAIA2O7oaOoKCgoKCk2gZ6gKAFiztR8A8PIW//9v9AxH1rMd36iTw7QRKKOuoKCgMMLwhEhvkYhXHV92IfnFcZVRV1BQUBi3IOJNtloUV8p2YNQDpm4ro66goKAwfuEGVt1N0MyZUQ+Wk3FvBMqoKygoKIwwyEQ7gXEnm57T/X9U7KjswqJgGoAy6goKCgojjcCqh1q5b8wNPcrYSVtXmrqCgoLCOAY5SslYE1PPG1ETHDJ1ZdQVFBQUxi08gakTP88ZUaauNHUFBQWF3QBh9ItcUxfhKE1dQUFBYfyCjLkjRL+ImjpByS8KCgoK4xiuyNSD33O63AQr+UVBQUFhHIMYeugo9c163lBMXUFBQWG3Q1L6f5L8ojR1BQUFhXEMklNqhTSy9VkUjIsv/e55bOkblq4nQ5bG0woKCgoKTYDJL2L0S5L8EnwEHnppK25Z8So27BrMfCzF1BUUFBRGGNVATiFVJYx+SWfqQYJpXRmmyqgrKCgojDBiTD34PV9DUw/rqmdvmlFTfjFNUwdwPYBFAMoALrUs6+Vg2UwA/8WtfiSAK4JuSAoKCgoKkGnqvpFOlF+C9ZhJr6MRUhZN/TwAJcuylgWNp68DcC4AWJa1CcApAGCa5jIAXwXww+yHV1BQUNjzIVZdDDNK0+PUG2mAlEV+WQ7gLgCwLOtRAEvEFUzT1AB8F8BHLcty6j8NBQUFhT0XYtw5Ee8kBh6uH5VrsiCLUe8G0MP97ZimKTL8twF4zrIsq45jKygoKOzW+MQvnsK8K26vuZ6TkCGaxMTFOPV65JcsRr0XQBe/jWVZtrDOewHclP2wCgoKCrs/fv/MxkzrVQWmTtEvboJVH2n5ZQWAswEg0NRXSdZZDGBl/YdXUFBQ2PORxLwTjbroKG1l9AuA2wCcYZrmSvjSzgdN07wIQKdlWTeZpjkNQJ9lWY0XK1BQUFDYgxHX1Impy9enKBmy+S2NfrEsywVwmfDzam75VvihjAoKCgoKEohVFxlTT7DqFC1DHZNarakrKCgoKDQBMSNUq1NTr0d+UUZdQUFBYYQRi1MP/p8kv8RK7yqmrqCgoDB+IMovxNCTmLojOErrgTLqCgoKCiMMkXnXMupVJ1r7pdXJRwoKCgoKTUDU1OnvpF4YbDkr1as0dQUFBYWm4HkeTrjmPvzqiXWZ1h+uOnj3TY/imfW7YsuIeRPEnqUixNK7iqkrKCgoNAnXAzbsGsI///qZTOu/srUfj6zZjk//6unYskSmLjHqBUPnepr6Vl2FNCooKCg0CbH1XC1Qv9EtfeXYMlFTD416fD9tBYMxe6eBVqXKqCsoKChI4NbppByu+ha4Z6gaW2bH5Jdkpt5RMOJMPeM5AMqoKygoKEhB7FrPSNXL1eSq4yJTt5mjNG7U2woGp6krR6mCgoJCS+DUadSH7ZCNV+woMxc1dTdFfuks5dn2ZNwVU1dQUFBoEoxFZ7SowxxT7x2OSjBi8pGTIr90l3Io207i8lpQRl1BQUFBArFJdC2UOXYuyipimQAnRX7pLuWZPm/X+WEBlFFXUFBQkMKtI/rF87wIU0+KdhH3LZNfunimzuQXpakrKCgoNIWQqdc2qJ4XdZSKRjxm5FPkl65SDlXHw1Ov78SND63xz6GV9dQVFBQU9kaEjtLa63oQ5BfBWMcKernhejldixj97lIeAPCO68NmcvVI6zWNummaOoDrASwCUAZwqWVZL3PLjwHwTfiqzyYA77Usazj7KSgoKCiMP9QTTugK8ovI1IeEcEfS2F0PKOZ02JVweWcpbpbrcZhmkV/OA1CyLGsZgCsAXEcLTNPUAPwQwActy1oO4C4AczMfXUFBQWGcwqkjnNA36slMfbDiCOv7OrzreSjkoma4LW9I958VWYw6GWtYlvUogCXcsoMBbAfwj6ZpPghgsmVZVuajKygoKIxTuF72yBPPA3NuAvH0/qGKLdm//+EQjXpJYtRF5p+GLEa9G0APv3/TNGl+MBXA8fDlmdMBnGaa5mmZj66goKAwTlFPhUTPQ4Sp8yGMnudhUJJt6rgePA8o5qJGvJSPm+WWauoAegF0cX/rlmXRZ2c7gJcty3oeAEzTvAvAYgD3Zj8FBQUFhfGHZjR12/Hw57U7MKEtj85iTmqU3UB+aS9Ejbpo5PlzyYIsRn0FgLcB+JVpmksBrOKWrQHQaZrmQYHz9EQAN2c+uoKCgsI4RdigQr7c4yy1GP3y/ftfxh+e3wzAd4QCQN7QUHU8FHM6yrYLx/WN+onzp+LMBTPwnfv8+JOihKk7LdbUbwMwbJrmSgDfAnC5aZoXmab5EcuyKgAuAfAL0zT/DGCdZVm3Zz66goKCwjhFrYJePHsWmbq1uY/9m4x93vDNbVcQsuh4HlwXMHQdnzrTZOvLmLpXh1GvydQty3IBXCb8vJpbfh+AYzMfUUFBQWE3QK3oFz623HOjBb1kMIKPQ3cph239ZTiOz9QNgVrLNPVWO0oVFBQU9jrU6g/KG1oPXiSjVEas9SCLqSuIQ3cCTV2cCUg19TocpcqoKygo7FUo2w7e2DVUcz2+85EfqZKc+u96PlMnli2LK6fOSJRc5LoeXC/8aFDmqjz6RTF1BQUFBSk+8z/P4Phr7ovVPBfhcvLLgf9yBz7y0yelywHfiJerDtoLvsGWMvXAaNM61WB7kmVWffHNePaqN49K9IuCgoLCHoP7V28BAAxVnFjiDw9HiH65J4hmIUQ0dQ+oOq40G5RAMktHEMJILe7I2HcU6YMQN+BKU1dQUFBIQDEwvGI9FhG1Oh9FNHXP88MVA+lEZphpP23E1AOhXBcqhskySutJPlJGXUFBYa8CxY0PSFL3edSOfuFrvfhMvSSRTgikqVOyUZUx9egRcroWqwzZ6jh1hQT0l+1IbKqCgsL4BzkiB8tOqgOSdSdKWEWMfqk6bsjUJeuTUSf55QFrK4B4aV9N02JsXckvo4SFV96NN137wFifhoLCXouv3fECvnHX6torciCDecuKV7H/Z+/A1r6ydD2KYBFb0RF4TX3Z1fdhW3+FMXUx+kXXgL8/bT4AYM6UDgDA14Pzlsk7OcHSq+iXUcTGHlU6XkFhrHDTQ2tw/QOv1LUNGfXb/rIBALAhIbyRCnolRcnI2HNbwMJFG5w3dFyweDbWXnMOJrTlI8tkkr0hGHUlvygoKCgkoChEvIismECGtJqQ+SMz6qUE+YU/hhhxIxpw/7foOmIp3zQoo66goLBXQRZdIgPFoVcTLKrMqFOMuUisecNdEOoCKPlFQUFBoQmITL1Sw2iLTaMJst9l2aAAkOMMucjUZROFmPyiHKUKCgp7E+5bvRk/f+y1TOuKTL0ezTy6PL5dmA0a3ZY30uJHRYxTB4CcEf2tnnZ2KqNUQUFht8eHfvwEAOA9x9VukZwXDGaiUa9hSG2J1k4fDHHTNE1dJr8YmmjUU08lur/sqyrUi5c29+FLv3u+Lj1MQUGhcQzWSCgC4k7Hxpl6svwiMut0TT2+byW/jFPcb23BLSteRe9w7YGmoKDQPF7fMVhzHdHgJjlCa0keck09YOrC70a9TF0fQfnFNE0dfmPpRQDKAC4NWtfR8k/B7360Nfjp7yzLsjKfwR4MCoVSTF1BYWRRMHRUHBevbx/EITO7U9cVDWQtR2kSZPJMKUe1X6K/12vUY5p6i5n6eQBKlmUtA3AFgOuE5UcDeJ9lWacE/ymDHoA0t3qmTgoKCvVj5oQSgGxMXXwfqd3cYMXGx3/+FLb0DkvXi+0nRVO3hQ9FqqYuscJ8nLqmtT75aDmAuwDAsqxHASwRli8G8FnTNB82TfOzmY+8F4DSi5VNV1AYWVCRrK398pR/HjGmHhj13/71Ddy+aiOu/YPPS2sZdVmVRzLqVVfU1LmQxjri1M89cl+865g5LXeUdgPo4f52TNPkZZv/gt/D9FQAy03TfGv2w+/ZIPmlHj1MQUGhfpCEkkWmEI01GXX6mYysyI5F29szVI3tm8IVU5l6BqNO0S+G5ldsbLX80gugi9/GsiwbAEzT1AB827KsbZZlVQDcDuCozEffw0EPVhl1BYWRBRnmLOn0seiX4AcP0aYYoiEVszxlRp2YumiDeU1djEtPa32naRoMXWu5/LICwNkAYJrmUgCruGXdAJ41TbMzMPCnAngyvou9E+QdV5r67oGv/P55XP/Ay7VXVBh3CNl27XctSX6hn6lnqGj8RUadxtRFyOq7EGTx7uQoNXT/uPUw9SzJR7cBOMM0zZXw68V/0DTNiwB0WpZ1k2ma/wLgfviRMfdalnVH5qPv4aBQKUXUdw/86OFXAQAfO+WgMT6TvQuDFRtteYMZ00ZQqWNWnCS/UJQanYXIjkXDvGuwEtt3Iaf7zDqmqSdfmyz6htY3dM036nXYkJpG3bIsF75mzmM1t/ynAH6a/ZB7D1T0i4JCOnYMVLD06nvxHx84BiccNLXh/YTySzamfug+3fA8D6s39XHyi48k+UXM8pQx9Zyhw9A0OEiXbnjI4uRpfV3TYOgq+WhUkGU6VHWVpq6gkIYdA2VUbBdvJNQ0z4p65BfH9dBRMHDXP56E7lIuJr804yjNG1pCiGIKU5dktEaYuq7VZUOUUW8QWRwXtop+UVBIBfmdkioh1rufrEydDHchZ7A4dXpPmVEX9iW+xrsGq2gTioMVAqYuIo2pp8kvukbyizLqI44sN1nFqSsopIOIT7NGnZAl+sV1w4SfYk6PhTSG+4r+IJ5j71AVk9qjXYzyhi6tuig2veBRtWXRL3rwfw2GpiU26pBBGfUGkdC2MIKq0tQVFFLBmHo9rX1SMFixsa1GApLjeYwJ5w0N/eUqfvboa3j4Jb/SCRFt8b3l/97cO4w3eoYxsb0QWScfOEpFpDF1WQnfXMRRmno58W3rW12BkEV+CbuRK6OuoCADGTRZWF8juPPZTbjz2U1Ye805Kcfk5RcdD724DXc/t5ktp2Xie8u/81/4zbMAgNmT2vD8xl72e17XpAY8SVN/97H74dKTDkhcX9Pk9dbToJh6g8hiqKssy22kz6Y5uK6XWH50POOXj7+e2AleYfdAq+WXLHA5pl7I6bF0fxbSKGHqFPbYO2RjamcBHzhhXmSdvKFnqrpIuPodR6C7lI/9zpi6piVWkUyCMuoNIkv0y+7iKL3yt8/h4H+9c7eqJrl+5yA+e+sqXPYzleu2O6MZ+aXR8eq4HnNmiin7QMiMZe8tGfqq4+LgGV3ICVp5I/KLDDonv2zYWV9kkDLqDSKLTm6PQUjjmq39WJehUh2Pnz7qtwGrxxnjuB5+snLtmDF8+mDujUz9oRe3YvWm3tor7gYgoy4WwMqCRsm943rMaIoVE4Fkpg6E51txXBRyekzvzhualKnXK6HQR0fXNGyoM9xTGfUGkWVAjUVBr1OvexAnfuP+hrYt2/Gqc0n41RPrcOVvn8MP/7SmoWM1C3pvvFg7gvGJC294BG/77sMt2df7bnkcZ337Ty3Z11iDNHWZs7D2tsnPnli843p4ddsAAGCgbGNTz7Avv3AhjSLSwizpmBXbRcHQY1mweV3O1Ot1dtJuDV3D9K5SXdsqo94g9sSQxnIdrHvXoJ940TscT8AYDRAb2l0Uo8fX7sCqDT21V9zLQMSnEUdp2ju4/2fvwF3PbsIND76CN137AKxNfbjghkew9Op7fflFT5ZfWIBDGlO3faYuknJd16RGXUP0t38682Ccesj0xPOntQ1dw9XnH45fXHpc4roiVPRLg8gkv+wmIY265n946jHqYqLGaGCo4qCvXI0wl93FqCvI4aSw4lqoRaxuX7WRMeRVG3rwQhCl4nkhExabUPvnEsweJLun8y3bJL+E2z/4mVMAyJ2i4mvyiVPnp567xskv3aU8jq+jhIJi6g2iruiXcW55aGCWJUX/k0CDW5Y9N1J43y2P4div3gsgvKe7k3NXIQ56RxpxlNYiSxqAfSa0AQA2cro0H6eekzB1FuCQJr84LoqCpj5ron8s2TtR72tC69cr2wDKqKfi6XW7Eo1GFgnQZtO4Vp5V68GMeh1MnQZ3vQ6gZvDntTtjxx8Jk75jIF59T2Fk4LTQUSqm7OsaMLnDDxfcGLSoA3yjTYY3Lxm/aSUHYpo6J6uw1P4WvBO037SaMUlQRj0BD1hbcO73V+BnQWSIiD2q9kswbhqRX0aTqRMqtssx9dbv/+gv38P6VNYD1/Vw6U+ewMqXt7X+pPZQMAPaiKYuGN22QtSoa5rG9s8z9bLtMsMrNngGQsMte8dJmpFp6iSZSMg/gNF7T5RRT8CmHv+lfma93LlVj/xST9eSsQCRgYbklyZHUH/ZRn/ZrmuboYrDanyMVPTLpgaMen/Fxh9f2IyP/HTvip2/+eFXccQX725oWyI+1UaiX4T3SmTqGsK6Kht7wuc5XHUYGZHJL+y9lTD1t3z7T/j4z5/iQhqTW9E1Axbd1cDwVkY9Ae1F34c8mGDoMiUfkUSQ8GSGqw6GJft3Xa9uQ5eEh17ciu/e+1Ls96vveAF/ed2XM2iqVx9T9//f7FRz4ZV3Y+GV9RmEwao94pFF4925PZ7w5d8/j97hxsZrM2UCRGJVzAvmTAuZdS9XJneo6rBxK5Nf0py3fWUbt6/aCMf1UDAMqVZO++4q5fC3S/bzT6VeTT34fyOkRRn1BLQHX/3BBOOahX2HX3z58iOu+gOO+tI9sd+/escLWHjl3VKDXy/ed8vjuO6eFyO/ua6HGx9ag7dfvxIAx9R3E/llsOIk+inqafuVhkaiMbxx7jsZaTRy75tp+SiOgThT11hZW772uR/S6P9b6ijNmOWaxNQpe3T2pHYsnNUdnEt9aIap1wxpNE1TB3A9gEXwW9ZdallWrJGjaZo3AdhhWdYV9Z/G+ANlmg1Wkph67X3UKuiVlI1561Pr2bFL+XhyxH89/jqKeR1vP2p27ZOQnRd3Psd+9Y8YCK6xnuQjYlaNOHKaxWDZYdfA39qXt/Tj9G8+iBveuxhnLZzZ1DGGEp57GpxRiMhp1UdrJOB4HvQ6zReTXxqJfhHus/iuaFoovwwIzzOUXySOUifb7EGWUQqEgQe6Fu+mlBWkzzfytLMw9fMAlCzLWgbgCgDXiSuYpvl3AA5v4PhjjiQ2TIZYLPZDfQl5Q530EqeFRmVB0n6vuHUVLv/vpxvaJxBlRVu4NPtyVf5iPfjiVibVEOj6m+krWS+IAQ1WbO4awmt5et0uAMAfntvU9LEGK+EM7Td/3YB5V9yO6/5gpW5jj0KYE2/I5l1xe0tmc61CI2y7mSYZMUepTFNP+FiE8ksyU+d1fpnxliUfAdEGF6yZdZ0fOya/jJCmvhzAXQBgWdajAJbwC03TXAZgKYAb6z/82GLV+h4c8vm7cM/zm2PLaIDyTP3Wp9bjyC/dg+fe6IkM4KTBXG1Q9w27mY8MK0vab5L88v5bHmdSDWEsInqIVQ1WHWn0Cxm8VoSU8c/d2tQHAHjujfR6Ky5z3o4cxGfX16CW3SzW7RjExTc/hr7hqKxRL5imzm1bsV3c9NArNesKiWOwJGjquqYlfmj1FKbONHWOqRcl5QQKhiYlNaFRTzv7GmiiDEYWo94NgA8BcUzTzAGAaZr7APgigI/XfeRxgKcC9vnQi1tjy5hR5zT1h4NQteff6I0MKNlY9st0Bv+u0wDSWGgkdpc/Pg+e9Sexonrkl7RU6pECsSo/+iUubdE1tkIR4qfrfGW+NIxGlJP4XOt5Zq3EN+95EX96aVuEEDXCtsMyAeG9/fljr+Frd6zGLSteTd2Wvxc5XYtVTNQ0oCLpKgSAa5KRnHzEP29Z4a/E6BdJvHr9jtLGy2BkMeq9ALr4bSzLIkv3TgBTAdwBX5q5yDTND9R/GmODMCwvfsfJWPDRL1QnwnY9wajH7zw/IBrVWJvpBiMaIL4CY5IhbsRROpp1sPPBizVQttk18EdnETkNSELiMxri5JfMRj1DBEezsy/xfjei/bcCtuTdaeQDL2PFNA531kgC4w9XkJS81bQU+YWYOtcBiWBLZg9FmVE3DKmoQh8XX35p7Hk3o2pmMeorAJwNAKZpLgWwihZYlvUdy7IWW5Z1CoBrAPzCsqwfN346o4u0+iU0FgbL4UtDX3XbcSMRLTKjzg+IeqUKOp16SuGKEA00Pw1NZOoJmroMLEFjFNNl6cUbqsodpbIs1w27hnDBD1bWNBDiI+LlF6ax1nge9URENQrxo5DkyB9p0HPn2XEjM5VQU+dYcfCe1SIZ/HtVyMl6g/oNJuSdiOj//jJeRgk/NI0xdRqnEUdp6pXEEWrqIyO/3AZg2DTNlQC+BeBy0zQvMk3zI3UfbZzBTZmus8wx7sGS/lZxRKYu2Z7brv73WIucQyOIMXW79kemPvkl+v9mkYXB0kd1kJNfPMmMiX+eNzzwCp54bSd+98wb6cf3ko0l7Zfu6XDVwa+fXB974cjQpb2HMv9NGjzPw5d+9zxe3Ozr+uKYGCujLot+ashR6khYcT6bUeePVzB0iPK4z9Q9TO0sxrZlZQKkyUfxj7iUqSc4SilMUuMdpXVS7xENabQsywVwmfDzasl6P67/8GMLMkhp8gsPYhBVx41MNWWDOSJ3NMjUm+nbGDPqWZh6jZeobDvMYSSrg729v4wJbXnkDL9De3/ZxuSOgnRfIiq2G0vzFmGw6BfOUcotp2fCx87TtdaSZMRnNCiVX/z//9vdFm5++FVM6yri5IOnceshOKfk5/bJX/4F07uKOO6AKannQ9jcW8YtK17F3c9twoorTpUw9bFxlMqkyzSjvuLlbVgyb1LM4WhL5Bd6z+pxlMqYOkW/TO0qxDKEs5QJiMwepI5SuVHP63GmPprY65KPPM/DvCtux4/+tCZkdtKssvi29FWv2m6E2cmmSPyAkNZlzkBxm5mqVwUHkZ1JU09nfXykBc1g6D6UbQeLv/JHfO42vyHvx37+JI7+cphY9fBL2/Dshh48s36XdN9ZQvPoGgbLdmhAuUuhy4pOpUkmSDfqMfmlnOwopWgY8RlmnVnVk31Jl0LPZrzIL2SMcxmM+rMbevCeHz2Gq++IccEEAxoY9Rrjn7/dhZweS4QjTb0kMchhQa/ABHrAF9+2AFM6CqGmzr0zk9rjfUQTk4+Y/BJq6g07SuvbzD9uA9vs1qDB+JXbX2DGTfa+ywwfPayq60UkF2nbqwhTj+8/bcDS6TTjhBT3z38gkvY7XENT57PyKKpA1Jt/+7Qvc/zxhS0A/A9ez2AV7735Mbz1uw/jb763QrpvmiX8zxPr8O6bHpWuQ9cwWA3ll/6yzYpvsSxXPc7UayVJxZl6slGn+yDmMLiSD40M9fSrJLaalHk5VkxdRoiSjPrWfj8XYk3QgYiHzIDS/avUIBk8sSokNHyuOi7yho6uUlSUkDH1D5ywP5bPn8qN6fB9mDO5PbbvYqJR981qM4l5tGkjocN7nVGPxJenpLrzA4YMfD5BfpGNZX5AyBxIaU7J0FHaOFMXp66R86lDfuFnIXz9DMbUqRFIQkOQiuNm6o5ETPQzv34Gj6zZnjD78dh58IP92K/dGzn2n9fuYOGq9Jxk02we4i0ZkMkvdtSo8x85ILujsJ6XXbzP4gd5rDX1LKGytK7sY2ZLrovGaj2O0qJEfnE9n2zkczpmdEdbwon11Ekyy+m69Jzq0dTzEudr3Wiis9deZ9R548aYekpNZfHfAEW/pGvm/DYyA1Vraukfp3WaOn8+iUZdIoHw6/KyARk4MmSk2YuGrWxnM+riLEFW0IyOuWHXkLzWdXDsZ9b3sOzPkKmnD3XxGVKo4L0vbMZLW/r94wf7oqxiaunHjp9Rfqn1gSFs7Svjf59cHxxbXjlw7KJf/POoFdrrr5ssgckiTfia5WngiZUf0ijs2/FQdVwUDA0zuqPOUjoVMsB06jldk4ZZygx0kqZfIJ0oAAAgAElEQVROHwr+chvOKK1rKx97nVHnH1QaU5c5Qum3aiz6JX7razHjVKbORb9c/t9/xRnffJAtSwtx4pdVhQ9PFqYuY1r8bz0ypi68ALHkmKqL3qGEomjcuqKeL7JgIDRs63YMye8550fYOVCNHKNW4TFRbhsOzueSnzzBeotWHRe247KPW6/I1Fsc3XnxzY/h+gdeAcAxWuFDP1byC2v5xl1zEgkhaU4aaULvlIREyYy653lY+co2eJ6HR17Zzn6Xaeq26wUhjTpmCM2bw4zS6DkZRpiFWnVd5rSVySzZNHX/t3pJ+6L9JgAAjpg1ob4NsRcadT4KhAak3FEaN/5ssDluzZDGKJOPL684yQyLj1O/7S8bGFOk30T0DFWxdttAZKpWcdzISxH5mCUYdXkUT7iPAY49szZkEkcXj+GqEzPQ9PHh901TbUr1ln0Iqo4HTQO29Zel6fH8PaVjZtfUo3/LDFTVdiMzLPG66B7UYldZI1VXBw5Zf99xZgyMPVOvNWMFQuMsewaOZAZCrF02m73tLxtw0Q8fw+d/82yk+mjBiMsvjusy+eWTp83H/OmdbJkhaOp09LzuN9agjHBabui+I5VH3pCXL8vrIVM/+4h90FXM4d3HzpGsmYxTD5mBlVecitMXzKhrO2AvMuq/+vM63Llqo+DATH7h+QEq6sXDVQdPvhYWuCKWt72/jLWBM6hWBmeaU5I5SiWGhTeeZBzP/d7DOOXaByLSR9XxIuy3lsYPJM045GyfXlRXYOoiyrYbY7QiywfCmUtHwXdoiQbTDV60A6Z2AABe3zEYWe64XuQjRsfMmuUoXnvVcWOzoqrrRe5H/ByDf9Q4ZCMNIQgxTb08ttEv/H1L0tQpK1teETH+gU9j6lSAbsXL2yO/pzH1vKFh/6kduOdTJ7NlrExAYIA9Zgt0Jtvw6+mahg+csD8uWBxWRs3pWipTB/y+pauuejMO4j4oWbFv0PO0XtSMU99T8M//+wwA4KHPvIn9ljY1t1OY+m/++oaUoSz/+v0YqjpYe805EY1QNBir1vdgzbZ+1IKM/VYFxp0zNKzdPhg7TsV2I46m6HZyoyL9iEgMORAa5lpMvWw7MU29bLso5ozI8Siksa1gAANxg0mGcP+pnXhl6wBeFSIphqpO5Pz6yn4lx6z1usVnZHPbsnMQZj9JTL0WGmndxrYVk4/GqEqjrJFE0j2m+klSR6mEFNB+ZI7SDmpeI8hOhZwRI2eOS5p6nLsmFfTKGRqqrhub4bFWdZytMAxNShpIZmoieK0p7DVGncCzpLB8bHy9yHSQhV3JnVX0Nx/iJvsoEN72vYdTz5EGkExqsQVGw4fg8u971XEjur0tkZ1E8OfZM1RF71A1cm8qaUw9JaJGNH7DVQfdpXzkWdAL3B4kIIkfAroX1Eh4QHCkDlbs2HSdj5KpFZkiLrYdLxY773nReHrRUZo1/CxLqGqSVh7T1FvUIateyAq6JV0/FUeTOatlH900pt4VGHWx5o1MfrGDmZVshhAW9IrKL+QopQ8vGXHaNX+MnK6hKrlk+niNVfesvUZ+IfA3msagVH6RMJCkl7FWSKOsPkkWiHHQ4jmIES5R+cWNyC+8MUhilPw1v+XbD+HEb9wfLbRU9Z2vruuxno8ypsVjuOrE5Bf62MgcpW2B/CJuQx8zaoQgXvtQxYlV5OsZqmauaS+TX2RMkX8m9OHpHa7i8C/ejYdeDBtOH/gvd+BLv3seQNy5nYXRb+qR90gdb9Ev/PkMVhw8uyHa09fzPPYsZSG69FwjGc9OnKl7noeNPUPM5yLKlzL5xXU9VG1X6qBlTTKY/ILgb19Tv/PZjQBCI07Mnt+VoWvSHJccY+rKqI8KskSBAFEDKRvAPKQhjQmOyS192Rsaf/7/no39FtEeBUMakUcE+YVnsaJNufz0g3Hi/KmRa34jMCr88f793pdw5FV/wNfvXh25J8NVJ9ZEgyBj6nRe/L7pJS0F8cDiNnT+RZZtGDduIlPvGarWfHYEWvyv5xyK9xw3B7YbZ+p0HADoKBiRaoJ9wzZeDhzaHnz9n0rHJs3s0pAUox0PGR1bTZ0/n3/+9dN463cfxlau8cpVv3seP165FoCceYd9fMMPL0lM/P2/6aE1WHb1fewei8+6KOlCZLteJIIFiDPuuPyiw/P8RjRAyLrpe8F/OHK6Lg11JPY/Vkx9r5NfZCGNsg+qrAlGMlOXGHWJzAMAb+waim/veplrL/PnLzrc+JdAdJSmMfWpXQUYulwfFCWgvrKNjbt8gz+5owDH9fDp/3katz+zka0jOkDjRt3BrU+tjxiu/nI10tEoztT93xlTFwzEYMWOZSD2DFXZM64lv9C1T2jLI2/oqDqu1JlNckdbIReyzODc+hLi8cVjZ8k/kK3jSnT+WlnAIwVZLPfm3nJkGQD84vHX2b9lRj0Sduu6KOoGC2/k1//jC34hNLGGCyEvaVjhBPILz9T3ndiG9TuHGOMWWbz4NzF02jf/nuqaPBvdUPJLHJf8+M/4+C+eGpF9//BPa9i/6UWWGWWZxpfkYNwomSonRb+8sSu+rviiphn1NKYeYea2k6ipi9draBoMTZN+tIaqstBCF/Ond2JGdwm268Xax/ESRdl2YjLScNXFp371ND57K6vijK/dsRoLvnA3uz6xPkpVYOriVP6q3z0fC3PsHa6GURoZ5Rdd05A3NNjCR5FA+nB7wWDPmMYKHT/eoCS6jywvuyycb9h2Yk7WsWPqcTZN4MfXTC6TU3ZNtmQ8s+QjLgKpP4jykXUgAnz5RbyrFdtlwQSE2ZP8iBKxnjq/Hx5iFyODyTD+R0RaeleQdEYb49Ko37t6S4T5tRK/lzBKKVOXlAlIYuof/I8/x+p1R41o+LuUqQsnIMs+EysFAnGjzr9gjica+eSPga5p0LlMOh6y4lMV20UhpwdOJTfG5nknX7nqSj4+yYaIzjmpzAExddFAPLO+BytfiYa5+fJLoN/XYuqcf8XQddhuAlMPrq29YHARQNEPkXgbxfuaJaRRpj8PlJ3MTL3quE3Xbk+DLDhAXAYAMydwRj1FfuH/zY8Xusd035MK4RWMuLGnBDKefc+e5NdwGSjHlwFpRl2L/J2W90AfkbHS1Pc6+YUHveiym+/WGGwi+GgNz4vGM/P75xs9s/PIwNyqjgtDNyIfizT5xTe28dhf8XwAf0ppaJr0PogyCOC/aPQyyG4JH5lQtuOGKK2mx4bgoxcvSObvo5ggv8jQiKNU0/ypvO3KmTpdW0cxFyZg1Sm/bNg5hPO+vwKT2vM46eBp+OAJ+8e2kRnkY776x1gp46R7ufRr96KQ0/HIZ0+TLm8WLOwwwZlfth3YjodpXWF6fi35xRY+krRNMWcwI5x0vYWcHulWBQA7g3IOhYhR95n6hl1+GLCoqRdj8ovwfz3K8NMKeo1Ge0MZxiVTHy3QPZe977L421THaiQqxZNq90DtiBZAXo9b1n0nztSjYYuyF0Z2PEP3mUe9TN3QNWl/18GIUXdj+5UZAgJJGCIjE+UX0VEqQw8f0ljDqHuc/JLTfWeZrFUcL794HiKx8EkGxxN+/tHDr+Kv63bhfmsrrgoiZEQkEYgdwoww6V5uH6hIZcFWIe2abcfFhTc8gsOuvDuSiFWWfKhkxENsQg2EIaxJ93iipDTuuh0+QaDYdgA4cb5f/96c2Q0Asb6mSUxdE0Ib05pLUz2Z0ezfy6MmUzdNUwdwPYBFAMoALrUs62Vu+fnw+5N6AG6yLOtHI3SuLYesKBFBHtKYzA6jBtUTMj+59SSGQnz4suQUxmK4l0Bkc8O2wNSFDw1/fjx0TUs26rIaLI6L9kIOFcjvB2/Uv3L7C7Hlz7/RK90uegxP+ndSSKMMvUOhpl7rG0CXrmsaY298UTFNQ2DoA0cpdx61PhgiY6tVqArIVvANyNYdqJkSsGn7BeSauu16eHp9T+T8Zk1sq83U6VlJ5BciQ7Lj3fDeo3GKOR3fv//l2LL2goG3HzWL/b147iQ8/rnTMC3ohpQ30jV1XRPkF0GLl0W/7A5M/TwAJcuylsE33tfRAtM0Dfi9SU8HsAzAZ0zTnDoSJzoSCKNfJI5SSfqz43pSRgBEGXhV0Jn5gSvVICXZjCJY13XJS0CIOka9xA9AolFPkV/4sVvmNHUZajW8+M598ZdPRLxxdjTkMYtR50MasztKwxedN+rUaGGQk18A3+jUanjSSBREVj28UuOjsjkhWqRZ2MyopxvqiuNi0X4TsWi/CdL66JHxLIlZFz8Eso/YYftOYB97EWcumBHrqDW9q8SMsVjQS8w+pQ+8JsgvupbM1HMspFF6SiOOLEZ9OYC7AMCyrEcBLKEFlmU5AA61LKsHwBT4ZUtq579L8PPHXsOFNz7SyKYRDFbszH0g71zlO01pXD352k6seNlPIOEfCL3wtuth3wnyegy8IbMdjznoDD2qVddyLMn+9o8dj+0WjUk5wtS9xA+AuH8jqGEhm4iQr4BqsgD+VDhvaIwBHjKzK7KNLCHmnCP2we8/uTx+gATIkouA0JhmNeokY/zo4TV4+KVtievSPdEC+QUA+jnpiQzDICe/AP6zrpUh2kjz4HqcnGmOZ7FGTqvAmLrk2NH8AwdFQ0fB0BMypD2WUCRGEwFxoy4jDGRoZbe5vZguRtSMfhFlFy1q5KVx6kI9mdFGFqPeDYBPE3NM02R3yrIs2zTNdwB4GsBDAGoXz5bgc7c9i8df3dHIprH9fPg/n2CNeoHkm0tjh4zu+T9Yiff86DH/twiDCAdbKa/jxPnxyUjUqIdMvZjT2b5e3NwnzRQUjay0BK6k9Ko4RR8WyhREYtpTmbqvq1MiEa8lU7VEnu30DduRWhvdbdHZiyzFPa9rmDsl3j0GCA0kD/EerN/pGyfqQMMbiG9ccARueO9iHD1nYmQbXlPf3FvGe29+THp8IJpdLGfq/qtC2i6d8xd+8yx2SSQqHlmm4dv7y/jGXauxbscg3nTtA3h9ezxKKglpZZxf3z7CRr1WDf6hKgo5HYWcnhj9QixbFvsuMnMZUydDK/NFtScweEIto64J8ksYp147+mU8x6n3AuCpmG5ZVuSttSzrVgCzABQAvK91p1c/qGUW/0LWYlJZHaVUm/mnlxwXWz8qv4QGtZDT2f7P/NZDkXKqhI09w1izNZzgyKbzYanb5NBEUdd3EtYVjYwvv+hwPA9nfushHPqFu9gyShzKc4O/r2yjEGTeAcDEmFGPv+iGridOkTslbEo0AOt2DqEo6WBDxz9r4Uzc+rETsG8QQlcw9Ij8Ugu8/EJTcn4MUdQNFdCicga/f2YjVqTMAIBsL/eVv30O1z/wCj768yfx6rYB/JJL2qkFGVumx7VrqBJb1ixkmcDR5eH17hysMKO+qXcYNz74SmRdxw17iMrG+D3Pb8ZirtetnKn7/2+EqRMxOSogBFlDGtPAMkrHMVNfAeBsADBNcykAljFimma3aZoPmqZZtCzLBTAAJHjPMmIkkilqpogLy3uHo+3SHC6CIumh8uzWdlzYrusbiAStmsf5P1iJU697MNxecr4rXtkeC5UUHbeiBJRUNlcmvxBTF6frJL8YnEPJj37RmOGdIBh1sdgW4A/0JA1e2jxB+LC9vn0Q+01ul+6DD0ujK5vSWUDPYLXmB50QcZQGx+DlF/ogDXFlAgh95XSmTo//3y44ImY06G/6EJKRlLHOJMiYOklII1Ebht9nLTlx12AVBUNnceRX3xltPs3LL7Ios8de3Y7tAxWcdsh0AHKmLoYVnjh/Khbs40e3yGaBPDRNw+1/vxw/+dCxAOKaei35RQa692MV/ZLFqN8GYNg0zZUAvgXgctM0LzJN8yOWZfUC+DmAh0zTfBj+O/WzZk6ov45O61JQvDH3U22mHl2+cdewUCaAGIS84hsQT9H3q8PprKM4LwHJmCkP2Ufo8//3LO5YtSkap57K1N3EdWPySxCnnhb9IhZLKhg6k39E5zHFB/OgDDwZpHW2g3O5b/Vm9AxV8fqOQew3qU3a0IQPS6PbPLWziL6ynRo+ySMapx5ILRXeqAvyC/cMkzo7EViJZ12L1VoXx0IYkZXptAHEmbrneczZOBJGnScw8uiXcNyVbRf5nI58Tv7sefkl9BuFF7+1r4yuUg5f/JvDEo8Xyi8+lh4wBftO9GdsHTWMOuA7WrtL/hgWe5HS0GIOUop6SdlfboyZes2QxoCBXyb8vJpbfhOAm1p1QrKONvWAZ1yEWvWrxRfojZ6haPQLp6knR3xEU/Jtx0U+cECKTstJHXlpD07AfyGTPkLrdw5iEpd88vW7VuN3T78RngPvKOX205aPJi3Jol/0hNovFKcuzlDyhp7I1F8LdNzff3I53vpdv8ww3bdvXHAE/vnXz0TWl81+qraLbf1lfOjHT+CEg6Zg/c5BLJk3ia3PXwOv6ZNxntJZgOeFceVsv46LnOQDw8ep0/nwY1GMfuG12lo9WB1u3+LsSjTqdE+z2AP6sIpM3XY9tv1ItLvjP3ZSoy68bwVDT3yvHdcLE8q494w0+C19ZUxszzNDKWXqkvFD67UV6suvrFX7hfhHGlNn9dTHcfTLqII3dt+596XUKcyTr+3AjoEKnt3Qg409vmOJpq2vbO1nrKpWWrboSP3zqzvwxNrQacuiXxwvsYHxkCB9+KxeD6JfonJCR8pAS5OKDF2LvDBrtg7gr+t2sb/LYqw8y8LU8Z+PvMacjbHko6D2S1pIoyxJgxn19miWI0k4vNRA9+3CJfvFjpGX3FPeL/Hi5n4MVBzGpoiZlfI6/v1dR+LI/UIHKV3Bgn26pS/e/M/diat+9zw8z8O8K27HD4IeoDwZIE2Ul5GIqVO/0nbBcZwG9sEIxgKPDsGoU1ZqlsgJcl6LhpX3R4w0U8+STFfI6YmlhKuuy5zQfD4I3d++YRsT2vJs/Ek19eA587eMjHoWpi6eKw+6lzSURI1dBiIwqvRuAP4F+eY9L+LxtckRMef/4BG866ZH8NbvPozlX78fQPhgP/Wrp3HJT/4MIEvZ1TBSBQCuf+AVbO4ts4fDx6nTb3z6MxBvkEFttDSN6jqH55DmbEmTijQJ0+OTg4bt+IcFAE4+2M+ie3ZDL7sOHrrua+aO67GkGvF8RDaU5+UXjil3lXKsK5Ghh/p0kmyVtKzqhPJRueow9kbnCwDdpTzOPXJWZDt6/qcdOh3PXfVmPHvVm2NT6tWbehmD/+Y9FoDwnvh+kLgevc/ENtagARDll2Sm7nkeC4/lJaypnUXp+n1lqh/jQdeAfzhtPj54wrzYehcsno2r33E4gDh7jRj1Fra729pXxrwrbsePuKJ4sqzby372ZOTvYk6Xlpx2gxkFn1D24Itb8eiaHZGZkF85M5mpyzgba7pSQ+oUIRp1ek40GyTGnia/sBIa41hTH1WI9TOS4nXphr24uT/yN/9xfHSN/0GQGcrffuIE9m/Xo3ot0WOJD8d2XeYw/P0nl+OzbzmErRtzlDoecrrO4tT58ENzhh9MdIo5LXZeaUbd0OI6Oj+ziTN1X2r42CkHRa5DmnwUyERdJflLIPoyI0ydM+rzpnSwD7PBZWcmyVb8Mnpxizk94uilDyY9D75SnghiuHlDR3shh85iLiZlDFXCZthkUFyOTdM586ywPW/g6vMPZ3+XuJc/TX6pOl7kg0H4wtsW4C0LZ8Yqf9K5esE1XH7GwfinM83IOpoGXPvORSy8M8bUubHWynZ3r233P9b/99dQ8pMZdRGFnI5r3nEEAGAKJx/SWKdZkO14eP8tjwOIhtBObCuwiCQxKupjpxyI7rbomNW0cL1ajlLZuRKufeci/Pu7j8RX374Qbz18HwDxsgEyjHVBr3Fo1KNT2aT7kmTsZavLNPWJbeHgIqMr2tNiXjTqIVOf0V3CKeZ0tm60ybPvqKKkHkeQXxbs242nPn8G3n/8vEznStB1LRbuyJ9zhKm7HnPsGmzG4Uauh+BHv/jyiyw9nV46HgVDZ9fMG/X9JofJWT5T97dNM+o6M+r+ulQBkV5MMu4hU0+eAtOV8dqo+HINVBz0BK3oaGZCq/jyi78tP/sydC0yZvLcyy9LqgmXuZEPBqEY1M4Jm0RE9+G4HovEENkj3UvWBSiVqbdOU5fxDZn8IqJg6Fg4awLeu3RO5P2k8Rg6SsOl7ZxE2d2WTxw//3zWIczA8hFDNDbrNurcuLlg8WwUcwbec9xc9uxk/YxF0JhXTD1AkgNRRD0Zd7KaLZ0cI/U8ebytKL8Q+5YhwtRdn6kz+UWYBeiahskdhYSG1/56Xzr3sIhWTEir9cHXlSFNPaeH6fxpTN2P0pFrlhPbCrEpbiGns3Ph7yX/MvLtvpJ8EQBw+KwJAMIyraW8EWvyTMek/fL/5+Eypq7FfiPwTJ0YYSROPdgv/0x1XYt8vGR+ABkqdmjU+eddyhsRh6/4YShXXfbhEA0aGTGqLS5G+ESYegs1dWmNpCwO3eA6qPkIgTH1XDT6xV83vGZqXJIVGjQ2a22v01Eqa1LNQ+aUFTHWnY92Y6Muv2HSOi6Sm8tHHbievHUZbcYbQ/4Fqya8PL6e7fqO0iCkURYnLmMffPibaPMrtptY3hWIGnw7CGnkmboTaP3xKo3hOrKP24S2fOyFzhuhUefZEP8y8kY3TVP/13MW4BcfPg7HzJ0MwNcrPS8eqlfMJL+E50cQH/+GXUP4yH8+ASBk6rSOpmlsqi/ei4hRTwjRE7Fh11Aov3BvGzF1WiZGqVQC6YzOiQdtU5Qw9T+9tJUVTCsYeiKTdl2POc6zQhwDtQwgWy8w6gXRqLMibXrkbyDK2ie256XvQwzsGcYbmWdFLaNNl5x2LrSPMbLpY2PUf7JybaLxFvVJ2X1Zu21AGqq1bsdgTK7pL9tStsJPaV3Pk2qDvDHf1l/Gpt7hSBIOL7nwx6gGZQJyXEhjJBEoYCSyAVTlDL64tGy76Bu20Z2ge0frqQfyi64zg7q1v4z5n7sz0v0JoDIByaN0gqSIGa+pt+ej7Dzcr9zAy/Z1/IFTY4ZfTGKKyy/xffGaughNA84/ejaA0CEpY+r0YeIZr+cJRj2jQXvrdx+OdFUilPIG61wPyBl10jFoL8UgoYef0Vx88+P45C//AsA3hrJEMAD43v0vY/nX78e6GrVhbnjwFbzzhpUA4lJokv9FBBn/PFf/5ba/rMe1f/Cd1DL5hb8muu+1Zke0tYbw3RSd5M0iDHFMXidLgtJIYtSbZAxUHFz52+fw9Ppd+OaFR8aWxzX1uGPwzG8/hI+dcmBs2xO/cT8OmNYR+e2dNzwSi1QR4XqhNsizJ5cz6ku+8kcAIrsO/80zaNv1nWN5Q4fterGQRmIkMkNHmnpO12Oacdl20TtcxeSOgrTOeZSph45SOs76nUPsenjwsdk8pnQUsH2ggolteewSEop4Rs47tXh5ik84StJE+eOKRmxAiNxg8ksGpi6bGXz9/CNYf1UCMTl61lRPPbZfRJ3IWeUXIDTY/Pn6TF1nhqwW8eAxPRjPdI1JVSIntudj10ugonev7/AzdZNwDZcByo8bTQPaiwa2D4RliZPAyy9O8G5c/t9Ps+UUp85fBz/DIOdqztCQpiZdeuL+eGFjLy5csh+WzJuEnz7yGguDbRWYo5R79y8//WAcydUdai8Y+MSbDsI5R+zT0mNnxegz9eDprw3C3kSIGaXiWBko26jYLiuAL0JMxHh9+wA2JEwz//L5M9BdysHj5Bc+PI8vD0DgX8yj50zEv55zKBbtN1HC1H3pQ6eQRm7AHrZvd2xfADUW9tfLGfHpZtl20DdsY6IQF06IMXXHd5TmWNU46Wa+/CKhFVM6/eN0t+WxZN7kyLJiTscxQTJQNB49ys5pt8klAjTpv4Fokou/PKqppzlKZdJA3tBi03GW0h1xlMqmANGZVVb5BQgbW9TL1JPu2bSg/g0z6gnz/IltBQxWndSY9w07sxUOswUfR0chx+5dLRmGxgedr+gPY1UaubBVfoYxd4pP1MQoKRHTu0r46SXHYVJHAYvnTsa333VUJg28HshY+D+cPp+FDfvLNPzTm00cGpQqGG2MulGn8ZeUsCHKMuKApAGxYyDeFg6IOxIHKk5iH8dJHQW0F3Jw3ZAZ8EWnZF1z+NPRNA2XnngApnUWhAYVvkHN8yGNQZz6de9chNMOnQEg7knns0ANXYv1Kq3YLnqHbXS35aXSA11DIWBEFK0TGlr5y00ZpSKmskYCOq582wLc9Y8nsmV5Q8d/fPBY3PvpkyPb5ASjzv7Nvfj7Tw1nUzzjpRea2L0YuZHFUUrjRayT7Z+bjvZi1KjTeAojVLIWbcr+6uwKIm2iRj2IfhF6cGY5xoyAqdO9SwoamNCeh+N6Uuc6bZO1NG/vsB05TlvBYGMwaUZBKOaixl/0X5Gj1HFc5uvi7QCFbtL9SCoMNxrIUiZgrDEGRt1/eZI0ddERKKYckyYntvUiSPtKpoRd6ZpvTGmg8VICOeN5JrStP/4xyRs6hipReYWclJoQ0jhvajjVFY2H43psplHMGbGRUw4cpd2lnFQioJeXemySs5YMbVLkjKHLWSE5EXXNP59DZobMo5DT0VnM4cBpncK+OEOuhZ8lfv+3fvR4fCjoy8mHBn7kpANx/tGz8aHl/jIxxb9g1DbqNIuRXY+MqYdp+aH8IjOm4ucw1rA4xcjTWI3KL2H0i+t60jGaTzCWVKlSD6KL6B0RCRDNOnl/0WvbB+B5HmuA/lpGo94zVI34GDoKYenlJN2aGHjovPbXFwkdr6l3FClLNvoBAUKmX6/zs5UQywaMR4y6USfWm8TUxd/FqSVj6pKiUYDccKUlSOgBk6ZBxGdUkhRSq3F03tCjBb2Czkd+9Es0sYk3BqJRsl0PFSd08IjDpmK76B2y0VXKSzVjCmks5HQupJGPbJHfhySmToaGH7+kb49+jOMAACAASURBVCaxSJGpaxL9e1JHgZU65def0JbHdRcuwuQO3xglOkq18LxF/PLDS/Hl8xZK2VxO19GWF2qtMKYe7lN2b0WDKUoAxaC8rAxU4Iy/xaW8zip4DtuOVBrLJ8wYjj9wSnhNhs4c7+K7MjmQzyj44C+v78TJ//YAbnxoDfPJ1GLqdE27BiuRd6utkGP3P2ksTA4+sNR9iT5ScaOus/NPK6FBREbMeh5NsOiXMTuD2hh1RynP1D3Pi33xxAdedXx2unOgijlT2hmz2jkgD+2TFeJPZ+papMkwn2hD7wg/K9giaQ+WF0LHiKnzBb2yGHWeqRdyukRTD5m6jKWSBFTI6ZHkoxwz6nKmThmlhI6CgYGKw5gybzxndJewfaCS4viUX5+4PhlFmUFIKhtbzCC/zJnSjounzJWeW87QWBjgtK4ijtpvIjNq0Th1CVMPxsJ9nz4ZVceLnbdhaOgwDEzrLOKS5ftj/oxOPL1uF679w4uhpp7A1Om55A0tEiUluzd//NRJOGh62N4gr4f1gMSxf0Agc63fOYS5UzpY+Yb7Vm9h6+xMmPESOos57LArPlPn9l/Kh9FPSR+zq85diA//5xNYPHdS5HrEWXqJc5TSfX7fsrn4zV/fwBGzJ4TXGoyZkZZfPnTC/oxYiGDvwji26mPA1MN/y5xDPUNx+eXCGx/FSf92P4CQqWeNZ68FXQtCGgOjvGh2POHn5odfZf+WOT/yRrRq4JW/fc6vHWNobCZQceKhdiLTdFyPdVz3mbrgOCzbKNsuuko5qVGtcsdwXJc19ZAxdf7QVE+dQPLHp844GABwHte4d0a3r+fuHJR/VHmWy5+iqHHTfZA5vVjp27LcUSo2Ac6KnK6zqXvBCLrxCExdS3CU0tM9YFonzJldsfuf0zW0F3LoKuXwoeX748T503Dx0nkAQqbOny/VxbHdUPqbPz3aFlAmvxwkrJMzdKbLi0b9oOm+NEYfLhoHpN9PbM/H3jcRpHGLRr1ghONKJj398VMn4YwFM7Dma2czJ3sov0SPybezq7ouzjliH3zp3IV4+soz8Z9BnXO6Vv46RgpfeNsCfOLU+dJlYfTL+MWYMXWAc1JxBlE01rbr4oWNfjKF2CSiEeQNDf/38bDui675lfPoxfrkqfNx2KzuSMgV4ZLl+zNDF91nfFBv7SsH9dR9bb5KrIZbVzQMtuumMnXS8335Jfl7XDD0oE+qx8rMGroWMeqlnME+ZHowoyCcfPA0fDqoN7L2mnMi+77qbxYid/vzOOGgKZCBvyYtQVMHwhdUdh2FILLkwRe3Cr8L0S91UpKcoTH5JW9ofulaOzoGdU1+TqI8Is4wDV0LnIfh712lHDQN2DEQd5T62/jdo+g5mDO78Hww1oFk+SVyTbrGchvEEg9zJncgb2jMqNPxqcjXzO4SrM19cF0vMUqEqkj2Cpo6/0GUMXX2AZaErIrvOBEPCncUxxB/rcDIG/U0hNEv49esj5mmDoSDMK00Lm/EBytOzc7ttXDgtE4ctm84paM0fjJ47UUDJx88XbrtMfMmxUqlAsmaYncp5ydcBKwZiIbCyeSXCmPqRtyoB3p+d5ucqfPnw4c00rGGJM4nIFp4i46dhDlT2vHD9y2JpV8//rnT8OhnT4tdE11DvB57bflF9F9kiX5JQ97gCowZ0b6ZfIKQ7N7W6kSU0/WI8xDwDVpXMcdi/MWPEF06zUjmz4g6nbNE2OQMjeU2iEy9lNcxe1I7Xt7iF71jRj2YIU/rKsLz0ksHdwaOy12DUaZezOnMwMmMepqhF3MsKOu56rqppTjEkNaxgIp+kYA36mSw09g3v37fsC0tONUMqDPRUNVBTvcjH5IcMZ1Fuc6WFLPcVcqz1Ggy6vyAFdmRr6mHjtJr3nEELlg8Gx8+cX8Ucjq29fvGoasod5QSCjkdL23px+Nrd7AXIadrkRoh/DXqWjzcrl5M7yph5oRS4scmrqnXll9EFAT5pZ6wQv8cdEwKnHdnL5wplV8MXR79UnPfhi+/iAZnYrufwEX75kH+h7/53goAwAFTo0Y97RmzdfTQUSq+G4WcjrlT2nHP85vxX4+/zsYgxf9TFE3PUBX/ctsqnPOdP+G17QN4dkMP3nXTI7j9mY2MkX7rjy9GfEuFnM7Gr0x+SftNzEXJ6TryuoY1WwfYeyi9ViIoY8iSwyqNY3YKNVFTfjFNUwdwPYBFAMoALrUs62Vu+bsB/CMAB8AzAD4WdEuSIiK/UBU+gWFECx2Fy/rL1ablF3HapGuaH6decZmhSwrRSkqLTgpn6yoGTN0ONfU0+SXK1HVM7y7h2ncuAuA3xLg3cHB1JYQ0svORJAOJ8ksxH/24iOF2jcJIuBdJ7EpmQJOSS0SmnrX2CCFnaJjWVcQT/3o6JrcX8LU7XogxdU2L6t0EWXTKv11wBHYMVHD1nath6Bo+esqBMbY8o7sYkT/+57JlrOaK+Py7Szl8+dzD8PnfPAcg6hD846dOkvox8kbcUXrGghl4x1GzUMwZuOIth+ABayte2tKP+YHGTkEB5B/pGariF4+9DgB4YWMvXtzcj0fX7MDWvjL7CLoecPdzm9hxC0aUqRdyOg6Z2YVn1vew3+LnSvJL9DoMXcNg1WFZrkkfM4rLp+e/cNboJ/ekNccYL8jyVpwHoGRZ1jIAVwC4jhaYptkG4CsA3mRZ1vEAJgB4a9rOeOYd9iSMvgizJ7Vx64SNG/qEBIhGID4SJr/YDktXTtIXk4x6ovzSlkc+F2XqPKsXB4gtRL/w4P/uKuVjRpJn3rxR5Evf8hE6fB0T6nwkO1a9iLMsOasOZy71M/W0aX8aaL9TO4vQdQ2FXFhgio9TB8IPO90WWVbmO5fsh2P2D5yAuo6TDp6G0xfMiKzDp+DrmoZj5k3G24/y68+Iz7CY13HxsnmYNbEtcg6A7yA9RsjqBQJHKTH1wKi/65j98Jag/vchM7sxtbOIwYrNQhLpYzW9K2TqhLLtsnaElMB2bHDcjVz3okIudJQWczqsL5+F33C+KtmzyTFHqegA1yIfzZpMXdfwwpfOwq0fPUG63khCViZgvCHLW7EcwF0AYFnWowCWcMvKAI63LIuCXXMA5MUmAjhemApMWZbitHHelDDj0HZcpv+2wqhT6juBOUorDtoK0dshDq62hKSHJCPUVcohb2ioOC7zBdQKaeQ1dR78C85r6mR0OrhMyQK3behc0iMhjXw5BLH2SzNxwFk1dWKX9RhmsfNRvUZdJgFVHT/5h49TB0KWXEuKoWiapJnI3MnhWBZXERkpPfN6QvdyehgGmeS47CgaGKw4sTwFYurbuexsvwSH/zoPVPyOU52lXKx+UkR+yem+U5wnBtIZmFx+SZKlYtfKaeptBaMp8tEo6FTHM2HPcle6AfRwfzumaeYAvym1ZVmbAcA0zU8C6ARwT9rObMfDfgETJwNNL/g/nj4fP3zfErxl4Uy2ftUJmXp/UPelUfz9qQfh238bLSKm62CaOm/MfnHpcbj/n05hf3//oqOxz4Q2yMAzY6rrAkQ1dZJfktLogXicOg/eyPNMnQoW8Y7LAnc+5ODL6VqklEEXV+hITI1P+nhlQVZNfe4Un8GexT3r8JzlEB2kdcsvkj6rAIIGKWH0CxBPbU86J0qWSZIM5kyJNg3hIf5NRrxWpiaPvCSkUbwvbXnfqIuJeaSp8wy84rhMLhqqOLBdD4auYa5Q9KtgGKwJs+w5yKJDaL2Yo1R4LknyW154/mOBPSWjtBcAHxyrW5bFnoppmrppmtcCOAPA+ZZlpYreruexAj1k1On/B03vxBkLZkQYiu26TP/tG66mtnurhQ+esD+mCL0hfabuG3X+uMcfNDUydU6ruMazuS+8dQH7N4t+cfzko4KhRwZDR8FgU22AmLoj1GvxwRv5zmKYzTc5yPDko3L4dfkKgfwUl28BZuiaEHrW+IAVwwFpT+L1zJ3SgVVfPBMXHTsnto9FsyfgmnccHvudoDcov8SZMWfUXdLUA4MajAVyGidpqW01mPocQX7hITr8RCOeiakHJSEu++mTeM+PHgMgY+o5DFbsGFOfHhh1vil037CNTUGC3UDFZpU+54hGPagyKTteEkh6FOs2ic8l6V5maY04Whj7M0hGlqexAsDZAGCa5lIAq4TlNwIoATiPk2FSQQOEposVQZrgDYzttE5TNyTGimqzDAtGvR7wRp0foF2lfOAodVG13ZixzBk6VlxxKv7jA8cACDV12UsyKTDe1FyB7AEZdb4pMs+cyCkmvjh8SVIxo7QZFpLM1OPX1FXKS4+laRouXLJf4jGacZTyCGVANxL94p+v//93HzsH7182F5dL8hOAkKknleKdw8svGZk6a4KRhanrOrb3V3AX58QUJaP2gpypT2zzZ3ybuSzpV4Lwx0NmdsHzfFJg6BrmTJEZ9eh51zzXYIOnXt8V+V2WyJW2/Vg6K4kYjWOinin56DYAZ5imuRL+B+qDpmleBF9qeQLAJQD+BOA+0zQB4N8ty7otbYdk1G0hpFF0hAG+oaPB3TdsZy7ML4NssFBtlqGqG9GZ60G0209U+87nfE296riJBZroZXdcFxXHlb7Msyb6rEp8MU85eBrOO3JfbOodxuNrdwTnI2HqwijsTtHUm0HSfurdf1rJVLqWpPuZBNHw0nirSuQXOt/2Qo4lYslQyussYkaGqZ0FZlTFZyB+ZPgaKP7f2Zj6mm39kd/E8dOWN7Cltxyvjpg3UMrpLOMVAF7Z6u/r0H26sXpTH3qHq75Rj8kvGqt3z88205D04RPvXVJiHUmGWUI9Rxq7tVEPwhMvE35ezf27bm8F6anEummwhd7tcF3bcUFRjP1lu6m6D7IXj+SX4YqDtmA6Wi94Zs1/OLpKeeT1UFOvVQTLcZHI1GdNlDcyaCsYuHjZPHzvvpfC/fFMnWv+wYNn6nw7u2YR76fp/78ZSUcEsftmmTo9D56piwXIap23pmlozxuJhkbTfIO4elOfJPlI7jdxmFGvfX1+qGo8Pp1HRzGHwaodWS8fJPwU80Yk/vxljqkDPpEydI29s/wxNgSVHsVlSUjK54jV0UkYixSxNaZMvUYS2njAqLuPdU1jDYbJqL/vlscB8I6i8LSqrgcnCNm6+eFX8dTrOxs+tkwCYHHqVaehpBsgWX7pDOLUXc8veVqr+4/NmHr8w7XvxOgHR9Sqk2LLqc6H+KLwMx49hWnWi1j0C1rv3CJW3WhII4GXXzyBqdOzyqLfthXSM3zJNyMao6QiZzaTX2oTGBlREO9LW8HAYNmJlKWmfZdyOitjAPhOzI6CgdmTQkPta+rRjmIFQ2cNNsRl9ZwrIGHqNYx6M8ESzYLJL+NYVR/12i/zpnZwDioPnuehYrvYZ0IJR+7nF9MSmTpfU/3FzX0Aoi20lh0wBUfOmYgfPPBK7Hi6hpheyoMvEyCL+rj+PUdjoqRHJw9+sOYNHd84/wj8/PHX/ezEgJ2sfGV7JP6eBw3ib9xl4a/rdrFCTDz2TZji0rZ8MpHDlV0YTNDU+RBITdNaxn6SkqLSkqWScMVbDkHB0NFRNLCBa8tWj+bMI8n5/OXbX8DyoJYN3QeSf9Jq7BA6ikZiGB4AFjkiyi/x8M/AqAdkJwvJoOc/uaPAGLc4g+koUEhjtMqi/38Dm/uirrD9JrdHmokYuh6RkQA/bJZ8YaLenoQs5ZqB5HtO72Grivk1ArJEu7X80mp0FAz2cKu2ywbGe5fOlTpC7CByZNHsCXh6fQ8GyjYKOR1FQ2fNg9sLBv7fWYdIjfqkIE07aRpNpXHF6BfC2YfX7jOYjzhHc7jwmP1w4TG+o49/wb74tsPk5xAM6r+u2xXsLz6oY+fGZIJo6B0Qras9xKJfRD05ur8RY+rBnw3YdFx2crwPLRAa9frLBETP7eg5fknYJ9buwLIDoka9Hqa+cNYEzEsxbEvmTcZvn34j1nUpKd29EaY+vasYGvUYU89hqBqNUx/mQmfFnrWzJrZF6ppTUThzZhf+Ejg5Czkdk9rz2DlYZZUcayFJLhONeC2mPpZGnZq+kzw1HjHqRh0IB6LtuhiuECvhmUH4UKtBVlsH1+aqYOjoKuWYUS+mMJrJQfPkUsILouu+o7ZV0S9io1t+2VFz4mV9gfgg7k0oh/qzS45jCSO0hYyp81UvTzhoqvQY4j1rVT2NNG25VSAjVK/8Ip7DtK4i/uG0+fj3e19i7FhMlsrC1L9/0dGpy89aOFMajy+LxgLCAIK0cU2g+50U0gr4RAoAdg2F2jkZRn7MFwy/Fk5HMRfpLkT34qaLl+Dimx/D6k19KOR03PkPJ8U6gT34mVMSS3m0FQx8591HYUpHAa7n4eKbfdk1p2v47SdOYDVwkggGBTKIyUujiQOmdeKXH16a+C6PB4ypUa84YR1zPvGHNzBOULmNkmuGqy4mtRvoLOVYSlSaw4yFAia8ILqmBWn8XsOZlLzhFiUBfllSUo84iGUt8wBg+fypidvyrI5s+qfOOBiXnri/9Bjzp8vb0NUraYiIOUqb2psczKg3UHhLBE3pdwUf0jCk0d93Kx28IpIYqVMHU6fz5I2wGGVCy3YOVDGju4jNveH44p/3hPY8tvaV0V4wIvuj85zWVWSkpWDomDmhxPxjhLlT0vX1v1m0b+w3Q9dwxOyJmD+9Ey9t6U+85xSx1TeGTB0Alh0oLzs9XjDqjlKACyWzXTYl5PXDCFMP5JfIoDX0SEZkGmOjllpJL4imaUwnbNyoh+crskF+WdJsQdSbk3qJSrc1osZY10IH9PzpnexjSC/mlI4C1l5zDuvlSWidUY9u380iFprabQRU66QVaeI0pQ9bzkUdu434ArIiiZHS9WXR1Gl88e+HGA7aFoyBHQOVWCMOYuqaFkoL7YVchPnz5+l4jfkz0kBjUxckRRETxgFT3x0wJkydDFHVceVMPZJ85MLhGtICZNQ5zS+FsVGfxqQXRNdCZ2Irol9E8IYnKfa6EcIpygR0nKmdRRYdIKszkySP0KqFJio08sch3PyBY3Dnqo2JJRYaAc3uGymRK4IMxa5BamTh/17rfrUCSR8MmmlljVMHEKtvz4Pkl6Gqw5LVCDTm2/IGIz7tBSNab597po1GHqWBiBCN6aQZDM2q0tpTKoyx/MIb9VIC07CD/p58T8dCTse5R+6LJ1/bib5hmw2CL7x1AYp5Hb1DNp5etwv9ZRuHz/IbYiS9ILqmMbmju8Hko7QBnsXwpEVO1AJdO81+pnUVpYWdcjWYp1idsOHzEYzgrIltuPTEA5rapwiK7sl6rr/88FKs2rBLuoyM+ta+MnMIAqEhk5XcbRVqDY0s18fLL+ceuS9+89c3YuvwBrqU91v6UdG80JDnWGRHe9FAez4uvwDhB2ckimnV+pCSQ3YsCnntThgjo05MPew4xEsTvKZedVy/gbLutx8bch3kDQ1vP2o2NveWcc2dq5lBot6aPKhGc7JRD+WOrJlx8etpzqjzL82bD5uBJXPjJVaTQB8EcnxN7SyyD2W0rroe+40HvazNGvXRKLZUb4XHZQdOSdRByai/vmMwVo4YiNb/bz38Y8yb0o7ffHx5bGnWKo2Ab9S/ct7CWME6IOpEbS/k8PSVZ3LVKP172N2WY85iP5lKZzXledJB8fytmCXd8fcn4onXdrC/xRINIjRNw9fPPxxHBVFLCnKMiVGn5r5VJ9TUk6Z7tkNt2fxC/ENVhwt9RPD/ZEMias6ycyHs07BRTz5+Fkcbf703XrwkZc0QYvTLwTN8rfTipXPRM1TF46/uwIHTQmdorRC9Wp3hs2I0ii01GtIoAxnynqEqDpgaOvkMIRFoJEAfjCmdRUyQ5EJk+cDSebYVcokRRry02c6FFAPhh2NiW55JUO3FsPKk7XoR5iyWU2gGC/btxgKuqqlWQ1MHgL89Jl4ATiGKMTHqANWydjEUhDQmauqu32E8b2hskJOGTu9bmu5J/RvTmDr9f4ZQM7qea0lClgiNZtgtSVX7TW6PNIk+f/Hs6DG43pyE/7lsGVYHjY7JqJx88LSGzwUYHabutFDX5SU3/t/0cXJH0KinNQoBsjF1mrV0JERWAVGmLvbYpXdqQlseWwMZkpyuvrTjRjX1wIc/Eqn69BqPh9ouuzPGzKhTcf9a0S9+Crc/wOglpnrhxNjSBhjVEE9ygtKxpneVMsUky5Aqv2RhWw0YQk1LZ94iaD1+5nDMvMmsm86siW144J9OiZQbbgQjGS1CaGVIYylvoJjTUbZdqfzSbFOWNJBBzpppKQPNsNI+AHxkjBjhRdtNaMszAsSMuqQnKN/2r9WoJb8oZMOYeRzyho4fr1yL597wmWK0EXL4UPliX2TU6SUgFpX2blPmXFI4IRnHfSY2VsyLzg1IaOGVYYA2M4izfhDCAlXJN2ve1I6mmfZ41NRrgfpw8kb90H18WaCVUTsiOoMIrnlTox9SKhORJWGLtUlMea68tNkuMHpi6m2FHKouGfVoGCz/TClOvRXSlwjRSa3QGMaMqVOH9VtWvAogGv3CP1TqfE6OUiAciDQNT8uGXHqAz0Tfs1SuxdEHZFpnY9ILEH4wPnRC3FGbLfqlAaYe/D/rNLietPdmMJLJOoRWMnXA74m7qXc4YtQvWb4/jpozEUskfUFbhaPnTMKNFy+OSV6/vmwZa1RRC1l8IXwkS7sgv9B2pXzYQSkqv0TlkO9edBT+7y8bYslrrQC9xyPqm94LMGZGXURJ0lcTCBMNcobOutFQ4pHDmHrygJ49Kao1i6BDNdPCrZDT8eJX3iI1aFnYZGPyS33rG3p0ljNS2N00dcA36k+8tjNi1HVdG1GDTnjzYfHyARPbC7HksCSEDWbSggXC+9QuyC+U0l/MGcwp3C606OOf6YzuEv4uoSZPs6DjjGzE0Z6PcWHUDV1LHJQDQWJQ3tBQDAYnTVvp4Tdjp4jpNtNsGUg2MGRE01660TCEMk19ZI4zepp6qxxqkzv8WVozH/axAskvWWctYlEx3qdlC5o6jd3R0riJqIgFxhTqQ02jbpqmDuB6AIsAlAFcalnWy8I67fAbTl9iWdbq+F7S0ZY3auqHvKOUsknJh9VMsSjatJnmG2nIS9iOiGYMoZeR1dDxxVodrcboaOr+g29VETLKVOzbDdPP6w1FFTNPQ6NuMGPaEdPUR8f1pph6a5DlaZ0HoGRZ1jIAVwC4jl9omuYSAA8BaHhOJkamyJ5pzgj7cnYz+SU9JCwLGFMfIZZWYGwn+VY3cvr1FuknRtdoglVWjAaru+wUf6iJ4XmNguL5p3RkkzzGE8jJ21XKlg0thj5SSGxHMcfegTYW/eKP2RFW7BhYwtfY9cDYI5DlrVgO4C4AsCzr0cCI8ygCeDuAnzZ6Elkz5yjzk9KFiak3ww7pA5IUHdMswhcj+RxbWZY2CdRcmO9oMxJI6y3aKnzslIPwsVMOatn+zj58Jm5472Kcfuj0lu1ztPDFcw/DknmTccy8bFmWInn5+JsOAjzgwiWzsXT/ybjf2sJY/2gz9b87+UA8+OLWcV3WdndAlqfVDVbkFgDgmKbJPgaWZa2wLGtdvQc++/DQQSRmzk0LkoD4QvQ5Q2dTTZJf6CVcekDjpTDJ0dRWGJmBS+zjgGnZWn7Vi6wT1Y09ZNRHlqnvjtA0DWctnNlwnsJYoruUx0XHzclMDET5pbuUx2fPPhTFnIH5M7rwkZPCCTdJh6OlqR+7/2S8/LWzMaWJSDSFbEa9FwBfr1O3LKtp8fH69yzGzKDRs1gWt61gYO015+ADx89jv+U5pk5TzeMPmoq115yDhUHRrkZQllSJbCUmtOdx48WLcfP7j0ld74LFs3HTxYsz75c+ilmThTYFRn2WMup7NcQ49TRkmWUqjD9kMeorAJwNAKZpLgWwqlUHp/6dSU4eXpbxmbpvgLO2z8qCcoaMvGbx5sNmxkqeirj2nYtwpiS8LQnvP34enrvqzZk18k+e6ssV07tG1lGqML5RT8G20cptUGgtsljH2wCcYZrmSvg5Lx80TfMiAJ2WZd3UzMHzQjKRiKhR55l664x6ljTr8QhN0+pyFH7ghP3xAUly1EhgwT7deMfRs0blWAr1oR7/DTH10fCTKLQONa2CZVkugMuEn2Nhi5ZlnVLvwSkyJImp806dvB7X1FsBpqnvZkZ9POOOfzhxrE9BQcB/fPAYPLl2Z13b5EehqJlC6zGmyUc1mTrf5MHQmAFurfwSL/2roLCn4U3mdLzJrC+6h7T0pEbSCuMTY+ruTyuEBUQNbSGn47wj/Sl954jIL7tf5IOCwkiCKozaKnB8t8I4YepylsxLIjO7S/j8Wxfg02cenKnLelaMhqNUQWF3BMkvtmLquxXGlJ4yTT0hPpg3tNO7ijB0LXPmXFaUq0pTV1CQgZKORrL7k0LrMaZGPV9DfhFDGkcCSlNXUJDjjAW+Br9gn+4aayqMJ4wL+SWLpj5SYJr6CJUJUFDYXXHWwn3wwpfOUoRnN8MYM/Xs0S8jBdLU1cBVUIhDvRe7H8bUqFPIVBJTJ8ll4ayRm/4t2s8vHlRPpp2CgoLCeMWYyi/kfkmrBX3P5SeNaA3wWz5wDNbtGByVSokKCgoKI40xNepUDD+ta8v8GV2Jy1qBCW15TGiiIJiCgoLCeMLYag4BVS+qcEIFBQWFlmBMjboXWPXibljHWkFBQWE8YkytKWUft6orvIKCgsLejrE16qSpK6OuoKDw/9u71xCpyjiO498VTfFWecGCLDPjr5EFFaZ52xdbZqJZ75LemFlBoBGlZVoQohR0wSLCYgmL3mT4wtTKQCM1LShDzf5ohYQQonkrc02XXjzP5LDt6Jmd2WfG4+8DCzPnsvPbP2f/M+fZs+eRqqjx8EsMoStPRESqorZNPXZ19XQRkeqocVMPXV09XUSkOs57nbqZdQHeAm4GWoCH3X1v0fqpwPPAaaDZrXfV/QAABO5JREFU3d/J+uKFMXUNv4iIVEeWT+rTgR7uPgZ4BnilsMLMugGvAXcBE4FHzCzz7Mn/janr76QiIlWRpZ2OAz4FcPetwG1F60YAe939sLufAjYBmSeoLNwZsau6uohIVWS5TUBf4GjR8zNm1tXdT7ez7jiQ+X/uF993I0MH9mLssAFZdxERkXPI0tSPAcU3YOkSG3p76/oAR7K++IDe3Zl39/Csm4uIyHlkGffYDNwDYGajgR1F63YD15tZPzO7BJgAfF31lCIikkmWT+qrgDvNbAvh6sOZZjYD6O3uy83sSeAzwhtEs7vv77y4IiJyLudt6u7eCjzWZvFPRetXA6urnEtERDpAl52IiOSImrqISI6oqYuI5IiauohIjqipi4jkSPKJp3ft2nXQzPalfl0RkQvcNVk2aijc/lZERC58Gn4REckRNXURkRxRUxcRyRE1dRGRHFFTFxHJkeSXNKZgZrcDL7l7o5ndArxNmF91OzDX3VvN7CngAaAVWOLuq4r2Hw5sAwa5+8l6y2lm/YAPCJOUHAJmu/uBGmacHzMeA15290+K9q+nWv4vZ+JadgOagSFAd2Ax8CPwHmF2x53A4zHrbOBRwty/i1PVtNKMqepZTs64/UBgCzCyuGadfXxWmrMj9czdJ3Uzmwe8C/SIi5YDT7j7eMIsTTPM7DJgDjCGML/q60X79yXMw9pSxzkXAJvcfRzwBrCkhhlHAjOA0THji2bWM+5fT7UslTNJLaMHgUMx12TgTeBVYGFc1gDcG+f5nQOMBSYBS82se/xZO7umlWZMVc9MOQHMbBLwOTCo+BskOj4rzVl2PXPX1IGfgfuLnl/l7lvi482EOVf/AvYBveJX4V2ygdAQFgAn6jUncAOwrs22tco4Atjo7ifjJ509wE11WMt2c5KulgAfAYuKnp8GbgW+jM/XAU3AKGCzu7e4+1FgL+lqWlFG0tUza04IvzdNwB+FjRMenxXlpAP1zF1Td/ePgX+KFv1iZhPj46mE5gjwG+E06DtgWVz2ArDG3X+o85zbgWnx8TSgZw0z7gAmmFkfM+sP3BGX11stS+VMUsuY8093P25mfYCVwEKgwd0L/wFYmOO31Ny/nV7TKmRMdWxmzYm7r3f3Q22+RZLjswo5y65n7pp6O2YCz5rZGuAAcJBwGnQlcC1wNTDdzEYRTpVmmdlG4ArCqVA95lwKDDGzL4DBhMZfk4zuvptwSrmOcCq7LWavq1qeI2fSWprZYGAD8L67f8jZsy84O8dvqbl/k9S0wozJ6pkxZynJjs8Kc5Zdz4uhqU8BHnL3KUB/YD1wGPgbaImn4keAy9x9mLs3unsj8Dth7LXuchLmgl3h7k3Ar4TTsppkjH/YGRDH/OYSDryd9VbLUjlJWEszG0RoHvPdvTku/t7MGuPjycBXwDfAeDPrYWaXEoaOktS00owkqmcZOduV6visNCcdqGcur35pYw+w1sxOABvcfS2AmTUBW82sFdhEaKK1VE7O64AVZgawH5hVq4xxbHKomX0LnAKedvczifKUkjmnmTnparkAuBxYZGaFcda5wLI4cftuYGXMtYzwy94FeK4zrxyqZsaE9cyUs5NeuxyV5iy7nrqhl4hIjlwMwy8iIhcNNXURkRxRUxcRyRE1dRGRHFFTFxHJETV1EZEcUVMXEckRNXURkRz5F2tAdJ7La7MIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "suicide_rate_per100k_annualized_by_monthyear['<14'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Group by time unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "tmp = raw_suicide_rate_per100k_annualized_by_monthyear.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "hidden": true
   },
   "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>&lt;14</th>\n",
       "      <th>14-18</th>\n",
       "      <th>19-25</th>\n",
       "      <th>&gt;25</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.332630</td>\n",
       "      <td>8.351772</td>\n",
       "      <td>14.487240</td>\n",
       "      <td>15.823328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.318148</td>\n",
       "      <td>7.499289</td>\n",
       "      <td>12.926622</td>\n",
       "      <td>14.513221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.349059</td>\n",
       "      <td>8.078998</td>\n",
       "      <td>14.519148</td>\n",
       "      <td>16.604678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.321206</td>\n",
       "      <td>7.871620</td>\n",
       "      <td>14.205629</td>\n",
       "      <td>16.349807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.316410</td>\n",
       "      <td>7.714503</td>\n",
       "      <td>14.520340</td>\n",
       "      <td>16.895665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            <14     14-18      19-25        >25\n",
       "month                                          \n",
       "1      0.332630  8.351772  14.487240  15.823328\n",
       "2      0.318148  7.499289  12.926622  14.513221\n",
       "3      0.349059  8.078998  14.519148  16.604678\n",
       "4      0.321206  7.871620  14.205629  16.349807\n",
       "5      0.316410  7.714503  14.520340  16.895665"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get by month\n",
    "suicide_rate_per100k_annualized_by_month = tmp.groupby('month').mean()[GROUP_ORDER]\n",
    "# preview\n",
    "suicide_rate_per100k_annualized_by_month.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Get by year\n",
    "suicide_rate_per100k_by_year = tmp.groupby('year').sum()[GROUP_ORDER]/12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "suicide_rate_per100k_by_year.plot(figsize=(12, 8), style='-*', color=COLORS)\n",
    "_ = plt.title('Suicide rate per 100k by year, {0}-{1}'.format(START_YEAR, END_YEAR), fontsize=16, weight='heavy')\n",
    "_ = plt.xticks(fontsize=14)\n",
    "_ = plt.xlabel('year', fontsize=14)\n",
    "_ = plt.yticks(fontsize=14)\n",
    "_ = plt.axhline(0.0, color='k')\n",
    "_ = plt.legend(fontsize=14)\n",
    "if SAVE_FIGS:\n",
    "    plt.savefig('figures/suicide_rate_by_year.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(\n",
    "    suicide_rate_per100k_by_year/suicide_rate_per100k_by_year.loc[1980]\n",
    ").plot(figsize=(12, 8), style='-*', color=COLORS)\n",
    "_ = plt.title('Suicide rate by year, relative to 1980', fontsize=16, weight='heavy')\n",
    "_ = plt.xticks(fontsize=14)\n",
    "_ = plt.xlabel('year', fontsize=14)\n",
    "_ = plt.yticks(fontsize=14)\n",
    "_ = plt.axhline(1.0, color='k')\n",
    "_ = plt.legend(fontsize=14)\n",
    "if SAVE_FIGS:\n",
    "    plt.savefig('figures/suicide_rate_rel_to_1980.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Plotting rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "year_range_str = ', ' + str(START_YEAR) + '-' + str(END_YEAR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "months_map = {\n",
    "    1: 'Jan.',\n",
    "    2: 'Feb.',\n",
    "    3: 'Mar.',\n",
    "    4: 'Apr.',\n",
    "    5: 'May',\n",
    "    6: 'June',\n",
    "    7: 'July',\n",
    "    8: 'Aug.',\n",
    "    9: 'Sep.',\n",
    "    10: 'Oct.',\n",
    "    11: 'Nov.',\n",
    "    12: 'Dec.',\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### All at once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "suicide_rate_per100k_annualized_by_month.plot(figsize=(12, 8), style='-*', color=COLORS)\n",
    "_ = plt.title('Annualized suicide rate per 100k' + year_range_str, fontsize=16, weight='bold')\n",
    "_ = plt.xticks(fontsize=14)\n",
    "_ = plt.xlabel('month', fontsize=14)\n",
    "_ = plt.yticks(fontsize=14)\n",
    "_ = plt.legend(fontsize=14, bbox_to_anchor=[0.16, 0.4])\n",
    "#_ = plt.axhline(1.0, color='gray')\n",
    "if SAVE_FIGS:\n",
    "    plt.savefig('figures/suicide_rate_by_month.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Normalized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Set 'normalize_by_mean = True' if you want to normalize relative to whole-year average\n",
    "# Set 'normalize_by_month = INTEGER' if you want to normalize relative to a specific month\n",
    "normalize_by_mean = True\n",
    "normalize_by_month = 0\n",
    "\n",
    "if (normalize_by_mean > 0) and (normalize_by_month > 0):\n",
    "    print \"Pick only one option silly\"\n",
    "    \n",
    "if normalize_by_mean:\n",
    "    title_str = 'relative to whole-year'\n",
    "if normalize_by_month > 0:\n",
    "    month_str = months_map[normalize_by_month]\n",
    "    title_str = 'relative to {0}'.format(month_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "if normalize_by_mean:\n",
    "    rel = (\n",
    "        suicide_rate_per100k_annualized_by_month\n",
    "        /suicide_rate_per100k_annualized_by_month.mean()\n",
    "    )\n",
    "elif normalize_by_month > 0:\n",
    "    rel = (\n",
    "        suicide_rate_per100k_annualized_by_month\n",
    "        /suicide_rate_per100k_annualized_by_month.loc[normalize_by_month]\n",
    "    )\n",
    "rel = rel.rename(index=months_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig, ax = plt.subplots(1)\n",
    "rel.plot(figsize=(12, 8), style='-*', color=COLORS)\n",
    "_ = plt.title('Suicide rate by month, ' + title_str + year_range_str, fontsize=16, weight='heavy')\n",
    "_ = plt.xticks(fontsize=14)\n",
    "_ = plt.xlabel('', fontsize=14)\n",
    "_ = plt.xticks(range(len(rel.index)), rel.index, fontsize=12)\n",
    "_ = plt.yticks(fontsize=14)\n",
    "_ = plt.legend(fontsize=14)\n",
    "#_ = plt.axhline(1.0, color='gray')\n",
    "if SAVE_FIGS:\n",
    "    if normalize_by_mean:\n",
    "        plt.savefig('figures/suicide_rate_by_month_relative_to_mean.png', dpi=300, bbox_inches='tight')\n",
    "    if normalize_by_month > 0:\n",
    "        filename = 'figures/suicide_rate_by_month_relative_to_{0}.png'.format(months_map[normalize_by_month])\n",
    "        plt.savefig(filename, dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "hidden": true
   },
   "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>&lt;14</th>\n",
       "      <th>14-18</th>\n",
       "      <th>19-25</th>\n",
       "      <th>&gt;25</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.332630</td>\n",
       "      <td>8.351772</td>\n",
       "      <td>14.487240</td>\n",
       "      <td>15.823328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.318148</td>\n",
       "      <td>7.499289</td>\n",
       "      <td>12.926622</td>\n",
       "      <td>14.513221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.349059</td>\n",
       "      <td>8.078998</td>\n",
       "      <td>14.519148</td>\n",
       "      <td>16.604678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.321206</td>\n",
       "      <td>7.871620</td>\n",
       "      <td>14.205629</td>\n",
       "      <td>16.349807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.316410</td>\n",
       "      <td>7.714503</td>\n",
       "      <td>14.520340</td>\n",
       "      <td>16.895665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.226064</td>\n",
       "      <td>6.300786</td>\n",
       "      <td>14.121849</td>\n",
       "      <td>16.525145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.224198</td>\n",
       "      <td>6.350977</td>\n",
       "      <td>14.682233</td>\n",
       "      <td>17.102449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.283978</td>\n",
       "      <td>6.704360</td>\n",
       "      <td>14.785563</td>\n",
       "      <td>16.962303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.266878</td>\n",
       "      <td>7.344847</td>\n",
       "      <td>13.996800</td>\n",
       "      <td>16.067070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.326658</td>\n",
       "      <td>7.930831</td>\n",
       "      <td>14.395082</td>\n",
       "      <td>16.041670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.312162</td>\n",
       "      <td>7.527163</td>\n",
       "      <td>13.681170</td>\n",
       "      <td>15.140154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.262226</td>\n",
       "      <td>6.617081</td>\n",
       "      <td>13.475807</td>\n",
       "      <td>14.987240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            <14     14-18      19-25        >25\n",
       "month                                          \n",
       "1      0.332630  8.351772  14.487240  15.823328\n",
       "2      0.318148  7.499289  12.926622  14.513221\n",
       "3      0.349059  8.078998  14.519148  16.604678\n",
       "4      0.321206  7.871620  14.205629  16.349807\n",
       "5      0.316410  7.714503  14.520340  16.895665\n",
       "6      0.226064  6.300786  14.121849  16.525145\n",
       "7      0.224198  6.350977  14.682233  17.102449\n",
       "8      0.283978  6.704360  14.785563  16.962303\n",
       "9      0.266878  7.344847  13.996800  16.067070\n",
       "10     0.326658  7.930831  14.395082  16.041670\n",
       "11     0.312162  7.527163  13.681170  15.140154\n",
       "12     0.262226  6.617081  13.475807  14.987240"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suicide_rate_per100k_annualized_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regressing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Set up data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "hidden": true
   },
   "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>&lt;14</th>\n",
       "      <th>14-18</th>\n",
       "      <th>19-25</th>\n",
       "      <th>&gt;25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-01-01</th>\n",
       "      <td>0.176692</td>\n",
       "      <td>6.714798</td>\n",
       "      <td>15.076880</td>\n",
       "      <td>15.312611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-02-01</th>\n",
       "      <td>0.201934</td>\n",
       "      <td>6.007977</td>\n",
       "      <td>15.036888</td>\n",
       "      <td>14.570182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-03-01</th>\n",
       "      <td>0.252417</td>\n",
       "      <td>6.596995</td>\n",
       "      <td>16.036681</td>\n",
       "      <td>16.444816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-04-01</th>\n",
       "      <td>0.227175</td>\n",
       "      <td>5.065550</td>\n",
       "      <td>15.636764</td>\n",
       "      <td>17.150124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-05-01</th>\n",
       "      <td>0.100967</td>\n",
       "      <td>6.066879</td>\n",
       "      <td>14.676962</td>\n",
       "      <td>17.131564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 <14     14-18      19-25        >25\n",
       "1980-01-01  0.176692  6.714798  15.076880  15.312611\n",
       "1980-02-01  0.201934  6.007977  15.036888  14.570182\n",
       "1980-03-01  0.252417  6.596995  16.036681  16.444816\n",
       "1980-04-01  0.227175  5.065550  15.636764  17.150124\n",
       "1980-05-01  0.100967  6.066879  14.676962  17.131564"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview\n",
    "suicide_rate_per100k_annualized_by_monthyear.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "df = suicide_rate_per100k_annualized_by_monthyear.copy()\n",
    "df['year_month'] = [pd.datetime.strftime(x, '%Y-%m') for x in df.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "hidden": true
   },
   "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>&lt;14</th>\n",
       "      <th>14-18</th>\n",
       "      <th>19-25</th>\n",
       "      <th>&gt;25</th>\n",
       "      <th>year_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-01-01</th>\n",
       "      <td>0.176692</td>\n",
       "      <td>6.714798</td>\n",
       "      <td>15.076880</td>\n",
       "      <td>15.312611</td>\n",
       "      <td>1980-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-02-01</th>\n",
       "      <td>0.201934</td>\n",
       "      <td>6.007977</td>\n",
       "      <td>15.036888</td>\n",
       "      <td>14.570182</td>\n",
       "      <td>1980-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-03-01</th>\n",
       "      <td>0.252417</td>\n",
       "      <td>6.596995</td>\n",
       "      <td>16.036681</td>\n",
       "      <td>16.444816</td>\n",
       "      <td>1980-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-04-01</th>\n",
       "      <td>0.227175</td>\n",
       "      <td>5.065550</td>\n",
       "      <td>15.636764</td>\n",
       "      <td>17.150124</td>\n",
       "      <td>1980-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-05-01</th>\n",
       "      <td>0.100967</td>\n",
       "      <td>6.066879</td>\n",
       "      <td>14.676962</td>\n",
       "      <td>17.131564</td>\n",
       "      <td>1980-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 <14     14-18      19-25        >25 year_month\n",
       "1980-01-01  0.176692  6.714798  15.076880  15.312611    1980-01\n",
       "1980-02-01  0.201934  6.007977  15.036888  14.570182    1980-02\n",
       "1980-03-01  0.252417  6.596995  16.036681  16.444816    1980-03\n",
       "1980-04-01  0.227175  5.065550  15.636764  17.150124    1980-04\n",
       "1980-05-01  0.100967  6.066879  14.676962  17.131564    1980-05"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = (\n",
    "    df\n",
    "    .set_index('year_month')\n",
    "    .stack()\n",
    "    .reset_index()\n",
    "    .rename(columns={\n",
    "        'level_1': 'age_group',\n",
    "        0: 'suicide_rate_per100k_annualized'\n",
    "    })\n",
    ")\n",
    "df['year'] = df['year_month'].str[:4]\n",
    "df['month'] = df['year_month'].str[5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "hidden": true
   },
   "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>year_month</th>\n",
       "      <th>age_group</th>\n",
       "      <th>suicide_rate_per100k_annualized</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1980-01</td>\n",
       "      <td>&lt;14</td>\n",
       "      <td>0.176692</td>\n",
       "      <td>1980</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1980-01</td>\n",
       "      <td>14-18</td>\n",
       "      <td>6.714798</td>\n",
       "      <td>1980</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1980-01</td>\n",
       "      <td>19-25</td>\n",
       "      <td>15.076880</td>\n",
       "      <td>1980</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1980-01</td>\n",
       "      <td>&gt;25</td>\n",
       "      <td>15.312611</td>\n",
       "      <td>1980</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1980-02</td>\n",
       "      <td>&lt;14</td>\n",
       "      <td>0.201934</td>\n",
       "      <td>1980</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  year_month age_group  suicide_rate_per100k_annualized  year month\n",
       "0    1980-01       <14                         0.176692  1980    01\n",
       "1    1980-01     14-18                         6.714798  1980    01\n",
       "2    1980-01     19-25                        15.076880  1980    01\n",
       "3    1980-01       >25                        15.312611  1980    01\n",
       "4    1980-02       <14                         0.201934  1980    02"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "SUMMER_MONTHS = ['06', '07', '08']\n",
    "df['is_summer'] = df['month'].isin(SUMMER_MONTHS)*1.\n",
    "VACATION_MONTHS = ['06', '07', '08', '12']    # arguably could include February\n",
    "df['is_vacation'] = df['month'].isin(VACATION_MONTHS)*1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "hidden": true
   },
   "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>year_month</th>\n",
       "      <th>age_group</th>\n",
       "      <th>suicide_rate_per100k_annualized</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>is_summer</th>\n",
       "      <th>is_vacation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>1984-08</td>\n",
       "      <td>&lt;14</td>\n",
       "      <td>0.150764</td>\n",
       "      <td>1984</td>\n",
       "      <td>08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>2001-06</td>\n",
       "      <td>19-25</td>\n",
       "      <td>11.891314</td>\n",
       "      <td>2001</td>\n",
       "      <td>06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1988-09</td>\n",
       "      <td>14-18</td>\n",
       "      <td>8.485869</td>\n",
       "      <td>1988</td>\n",
       "      <td>09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>1990-02</td>\n",
       "      <td>19-25</td>\n",
       "      <td>12.913030</td>\n",
       "      <td>1990</td>\n",
       "      <td>02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1125</th>\n",
       "      <td>2003-06</td>\n",
       "      <td>14-18</td>\n",
       "      <td>4.709074</td>\n",
       "      <td>2003</td>\n",
       "      <td>06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year_month age_group  suicide_rate_per100k_annualized  year month  \\\n",
       "220     1984-08       <14                         0.150764  1984    08   \n",
       "1030    2001-06     19-25                        11.891314  2001    06   \n",
       "417     1988-09     14-18                         8.485869  1988    09   \n",
       "486     1990-02     19-25                        12.913030  1990    02   \n",
       "1125    2003-06     14-18                         4.709074  2003    06   \n",
       "\n",
       "      is_summer  is_vacation  \n",
       "220         1.0          1.0  \n",
       "1030        1.0          1.0  \n",
       "417         0.0          0.0  \n",
       "486         0.0          0.0  \n",
       "1125        1.0          1.0  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "groups = df['age_group'].unique()\n",
    "years = df['year'].unique()\n",
    "dfs_by_group = {\n",
    "    group: df[df['age_group'] == group]\n",
    "    for group in groups\n",
    "}\n",
    "dfs_by_year = {\n",
    "    group: {\n",
    "        year: df[(df['age_group'] == group) & (df['year'] == year)]\n",
    "        for year in years\n",
    "    }\n",
    "    for group in groups\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Formulas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "BY_MONTH_FML = 'suicide_rate_per100k_annualized ~ C(month, patsy.Treatment(reference=\"09\"))'\n",
    "BY_MONTH_W_FE_FML = 'suicide_rate_per100k_annualized ~ C(month, patsy.Treatment(reference=\"09\")) + C(year)'\n",
    "SUMMER_FML = 'suicide_rate_per100k_annualized ~ is_summer'\n",
    "SUMMER_W_FE_FML = 'suicide_rate_per100k_annualized ~ is_summer + C(year)'\n",
    "VACATION_FML = 'suicide_rate_per100k_annualized ~ is_vacation'\n",
    "VACATION_W_FE_FML = 'suicide_rate_per100k_annualized ~ is_vacation + C(year)'\n",
    "\n",
    "PERCENT_FML = 'np.log(suicide_rate_per100k_annualized + 1) ~ C(month, patsy.Treatment(reference=\"09\"))'\n",
    "PERCENT_W_FE_FML = 'np.log(suicide_rate_per100k_annualized + 1) ~ C(month, patsy.Treatment(reference=\"09\")) + C(year)'\n",
    "PCT_SUMMER_FML = 'np.log(suicide_rate_per100k_annualized+1) ~ is_summer'\n",
    "PCT_SUMMER_W_FE_FML = 'np.log(suicide_rate_per100k_annualized+1) ~ is_summer + C(year)'\n",
    "PCT_VACATION_FML = 'np.log(suicide_rate_per100k_annualized+1) ~ is_vacation'\n",
    "PCT_VACATION_W_FE_FML = 'np.log(suicide_rate_per100k_annualized+1) ~ is_vacation + C(year)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "regs_by_month = {}\n",
    "regs_by_month_w_fe = {}\n",
    "regs_summer = {}\n",
    "regs_summer_w_fe = {}\n",
    "regs_vacation = {}\n",
    "regs_vacation_w_fe = {}\n",
    "regs_percent = {}\n",
    "regs_percent_w_fe = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Whole time period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "for group, tmp_df in dfs_by_group.iteritems():\n",
    "    regs_by_month[group] = sm.formula.ols(data=tmp_df, formula=BY_MONTH_FML).fit()\n",
    "    regs_by_month_w_fe[group] = sm.formula.ols(data=tmp_df, formula=BY_MONTH_W_FE_FML).fit()\n",
    "    regs_summer[group] = sm.formula.ols(data=tmp_df, formula=SUMMER_FML).fit()\n",
    "    regs_summer_w_fe[group] = sm.formula.ols(data=tmp_df, formula=SUMMER_W_FE_FML).fit()\n",
    "    regs_vacation[group] = sm.formula.ols(data=tmp_df, formula=VACATION_FML).fit()\n",
    "    regs_vacation_w_fe[group] = sm.formula.ols(data=tmp_df, formula=VACATION_W_FE_FML).fit()\n",
    "    regs_percent[group] = sm.formula.ols(data=tmp_df, formula=PERCENT_FML).fit()\n",
    "    regs_percent_w_fe[group] = sm.formula.ols(data=tmp_df, formula=PERCENT_W_FE_FML).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "regressor_order = [\n",
    "    'C(month, patsy.Treatment(reference=\"09\"))[T.01]',\n",
    "    'C(month, patsy.Treatment(reference=\"09\"))[T.02]',\n",
    "    'C(month, patsy.Treatment(reference=\"09\"))[T.03]',\n",
    "    'C(month, patsy.Treatment(reference=\"09\"))[T.04]',\n",
    "    'C(month, patsy.Treatment(reference=\"09\"))[T.05]',\n",
    "    'C(month, patsy.Treatment(reference=\"09\"))[T.06]',\n",
    "    'C(month, patsy.Treatment(reference=\"09\"))[T.07]',\n",
    "    'C(month, patsy.Treatment(reference=\"09\"))[T.08]',\n",
    "    'C(month, patsy.Treatment(reference=\"09\"))[T.10]',\n",
    "    'C(month, patsy.Treatment(reference=\"09\"))[T.11]',\n",
    "    'C(month, patsy.Treatment(reference=\"09\"))[T.12]',\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pretty_w_stars = sm.iolib.summary2.summary_col(\n",
    "    results=regs_by_month_w_fe.values(),\n",
    "    model_names=regs_by_month_w_fe.keys(),\n",
    "    regressor_order=regressor_order,\n",
    "    drop_omitted=True,\n",
    "    stars=True\n",
    ")\n",
    "\n",
    "pretty = sm.iolib.summary2.summary_col(\n",
    "    results=regs_by_month_w_fe.values(),\n",
    "    model_names=regs_by_month_w_fe.keys(),\n",
    "    regressor_order=regressor_order,\n",
    "    drop_omitted=True,\n",
    "    stars=False\n",
    ")\n",
    "\n",
    "def rename(params_table, months_map=months_map):\n",
    "    mapping = {}\n",
    "    for k in params_table.index:\n",
    "        if 'C(' in k:\n",
    "            _, month = k.split('[T.')\n",
    "            month = int(month[:-1])\n",
    "            mapping[k] = months_map[month]\n",
    "    return params_table.rename(index=mapping)[GROUP_ORDER]\n",
    "\n",
    "pretty_w_stars = rename(pretty_w_stars.tables[0])\n",
    "pretty = rename(pretty.tables[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# TODO: SEs here are fucked up. notice they're same for all months for a given age group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "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>&lt;14</th>\n",
       "      <th>14-18</th>\n",
       "      <th>19-25</th>\n",
       "      <th>&gt;25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jan.</th>\n",
       "      <td>0.0658***</td>\n",
       "      <td>1.0069***</td>\n",
       "      <td>0.4904**</td>\n",
       "      <td>-0.2437**</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(0.0184)</td>\n",
       "      <td>(0.1761)</td>\n",
       "      <td>(0.2038)</td>\n",
       "      <td>(0.1176)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb.</th>\n",
       "      <td>0.0513***</td>\n",
       "      <td>0.1544</td>\n",
       "      <td>-1.0702***</td>\n",
       "      <td>-1.5538***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(0.0184)</td>\n",
       "      <td>(0.1761)</td>\n",
       "      <td>(0.2038)</td>\n",
       "      <td>(0.1176)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar.</th>\n",
       "      <td>0.0822***</td>\n",
       "      <td>0.7342***</td>\n",
       "      <td>0.5223**</td>\n",
       "      <td>0.5376***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(0.0184)</td>\n",
       "      <td>(0.1761)</td>\n",
       "      <td>(0.2038)</td>\n",
       "      <td>(0.1176)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr.</th>\n",
       "      <td>0.0543***</td>\n",
       "      <td>0.5268***</td>\n",
       "      <td>0.2088</td>\n",
       "      <td>0.2827**</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(0.0184)</td>\n",
       "      <td>(0.1761)</td>\n",
       "      <td>(0.2038)</td>\n",
       "      <td>(0.1176)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>0.0495***</td>\n",
       "      <td>0.3697**</td>\n",
       "      <td>0.5235**</td>\n",
       "      <td>0.8286***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(0.0184)</td>\n",
       "      <td>(0.1761)</td>\n",
       "      <td>(0.2038)</td>\n",
       "      <td>(0.1176)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>-0.0408**</td>\n",
       "      <td>-1.0441***</td>\n",
       "      <td>0.1250</td>\n",
       "      <td>0.4581***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(0.0184)</td>\n",
       "      <td>(0.1761)</td>\n",
       "      <td>(0.2038)</td>\n",
       "      <td>(0.1176)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>-0.0427**</td>\n",
       "      <td>-0.9939***</td>\n",
       "      <td>0.6854***</td>\n",
       "      <td>1.0354***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(0.0184)</td>\n",
       "      <td>(0.1761)</td>\n",
       "      <td>(0.2038)</td>\n",
       "      <td>(0.1176)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug.</th>\n",
       "      <td>0.0171</td>\n",
       "      <td>-0.6405***</td>\n",
       "      <td>0.7888***</td>\n",
       "      <td>0.8952***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(0.0184)</td>\n",
       "      <td>(0.1761)</td>\n",
       "      <td>(0.2038)</td>\n",
       "      <td>(0.1176)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct.</th>\n",
       "      <td>0.0598***</td>\n",
       "      <td>0.5860***</td>\n",
       "      <td>0.3983*</td>\n",
       "      <td>-0.0254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(0.0184)</td>\n",
       "      <td>(0.1761)</td>\n",
       "      <td>(0.2038)</td>\n",
       "      <td>(0.1176)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nov.</th>\n",
       "      <td>0.0453**</td>\n",
       "      <td>0.1823</td>\n",
       "      <td>-0.3156</td>\n",
       "      <td>-0.9269***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(0.0184)</td>\n",
       "      <td>(0.1761)</td>\n",
       "      <td>(0.2038)</td>\n",
       "      <td>(0.1176)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dec.</th>\n",
       "      <td>-0.0047</td>\n",
       "      <td>-0.7278***</td>\n",
       "      <td>-0.5210**</td>\n",
       "      <td>-1.0798***</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>(0.0184)</td>\n",
       "      <td>(0.1761)</td>\n",
       "      <td>(0.2038)</td>\n",
       "      <td>(0.1176)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            <14       14-18       19-25         >25\n",
       "Jan.  0.0658***   1.0069***    0.4904**   -0.2437**\n",
       "       (0.0184)    (0.1761)    (0.2038)    (0.1176)\n",
       "Feb.  0.0513***      0.1544  -1.0702***  -1.5538***\n",
       "       (0.0184)    (0.1761)    (0.2038)    (0.1176)\n",
       "Mar.  0.0822***   0.7342***    0.5223**   0.5376***\n",
       "       (0.0184)    (0.1761)    (0.2038)    (0.1176)\n",
       "Apr.  0.0543***   0.5268***      0.2088    0.2827**\n",
       "       (0.0184)    (0.1761)    (0.2038)    (0.1176)\n",
       "May   0.0495***    0.3697**    0.5235**   0.8286***\n",
       "       (0.0184)    (0.1761)    (0.2038)    (0.1176)\n",
       "June  -0.0408**  -1.0441***      0.1250   0.4581***\n",
       "       (0.0184)    (0.1761)    (0.2038)    (0.1176)\n",
       "July  -0.0427**  -0.9939***   0.6854***   1.0354***\n",
       "       (0.0184)    (0.1761)    (0.2038)    (0.1176)\n",
       "Aug.     0.0171  -0.6405***   0.7888***   0.8952***\n",
       "       (0.0184)    (0.1761)    (0.2038)    (0.1176)\n",
       "Oct.  0.0598***   0.5860***     0.3983*     -0.0254\n",
       "       (0.0184)    (0.1761)    (0.2038)    (0.1176)\n",
       "Nov.   0.0453**      0.1823     -0.3156  -0.9269***\n",
       "       (0.0184)    (0.1761)    (0.2038)    (0.1176)\n",
       "Dec.    -0.0047  -0.7278***   -0.5210**  -1.0798***\n",
       "       (0.0184)    (0.1761)    (0.2038)    (0.1176)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretty_w_stars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbf965f88>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pretty.loc[[k for k in pretty.index if k != '']].astype(float).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# LOTS MORE YOU CAN DO HERE JUST FOR LOOKSIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                   OLS Regression Results                                  \n",
      "===========================================================================================\n",
      "Dep. Variable:     suicide_rate_per100k_annualized   R-squared:                       0.161\n",
      "Model:                                         OLS   Adj. R-squared:                  0.142\n",
      "Method:                              Least Squares   F-statistic:                     8.181\n",
      "Date:                             Fri, 06 Aug 2021   Prob (F-statistic):           3.72e-13\n",
      "Time:                                     09:15:36   Log-Likelihood:                -884.41\n",
      "No. Observations:                              480   AIC:                             1793.\n",
      "Df Residuals:                                  468   BIC:                             1843.\n",
      "Df Model:                                       11                                         \n",
      "Covariance Type:                         nonrobust                                         \n",
      "===================================================================================================================\n",
      "                                                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "Intercept                                           7.3448      0.245     30.030      0.000       6.864       7.825\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.01]     1.0069      0.346      2.911      0.004       0.327       1.687\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.02]     0.1544      0.346      0.447      0.655      -0.525       0.834\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.03]     0.7342      0.346      2.122      0.034       0.054       1.414\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.04]     0.5268      0.346      1.523      0.128      -0.153       1.206\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.05]     0.3697      0.346      1.069      0.286      -0.310       1.049\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.06]    -1.0441      0.346     -3.018      0.003      -1.724      -0.364\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.07]    -0.9939      0.346     -2.873      0.004      -1.674      -0.314\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.08]    -0.6405      0.346     -1.852      0.065      -1.320       0.039\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.10]     0.5860      0.346      1.694      0.091      -0.094       1.266\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.11]     0.1823      0.346      0.527      0.598      -0.497       0.862\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.12]    -0.7278      0.346     -2.104      0.036      -1.407      -0.048\n",
      "==============================================================================\n",
      "Omnibus:                       16.132   Durbin-Watson:                   0.438\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               13.724\n",
      "Skew:                           0.340   Prob(JB):                      0.00105\n",
      "Kurtosis:                       2.527   Cond. No.                         12.9\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print regs_by_month['14-18'].summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                         OLS Regression Results                                        \n",
      "=======================================================================================================\n",
      "Dep. Variable:     np.log(suicide_rate_per100k_annualized + 1)   R-squared:                       0.165\n",
      "Model:                                                     OLS   Adj. R-squared:                  0.145\n",
      "Method:                                          Least Squares   F-statistic:                     8.398\n",
      "Date:                                         Fri, 06 Aug 2021   Prob (F-statistic):           1.51e-13\n",
      "Time:                                                 09:15:39   Log-Likelihood:                 139.66\n",
      "No. Observations:                                          480   AIC:                            -255.3\n",
      "Df Residuals:                                              468   BIC:                            -205.2\n",
      "Df Model:                                                   11                                         \n",
      "Covariance Type:                                     nonrobust                                         \n",
      "===================================================================================================================\n",
      "                                                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "Intercept                                           2.1080      0.029     72.778      0.000       2.051       2.165\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.01]     0.1069      0.041      2.609      0.009       0.026       0.187\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.02]     0.0148      0.041      0.360      0.719      -0.066       0.095\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.03]     0.0790      0.041      1.930      0.054      -0.001       0.160\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.04]     0.0570      0.041      1.391      0.165      -0.024       0.137\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.05]     0.0408      0.041      0.996      0.320      -0.040       0.121\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.06]    -0.1343      0.041     -3.278      0.001      -0.215      -0.054\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.07]    -0.1276      0.041     -3.115      0.002      -0.208      -0.047\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.08]    -0.0820      0.041     -2.001      0.046      -0.162      -0.001\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.10]     0.0660      0.041      1.611      0.108      -0.014       0.146\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.11]     0.0216      0.041      0.527      0.599      -0.059       0.102\n",
      "C(month, patsy.Treatment(reference=\"09\"))[T.12]    -0.0954      0.041     -2.328      0.020      -0.176      -0.015\n",
      "==============================================================================\n",
      "Omnibus:                       37.897   Durbin-Watson:                   0.416\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               12.960\n",
      "Skew:                           0.016   Prob(JB):                      0.00153\n",
      "Kurtosis:                       2.196   Cond. No.                         12.9\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print regs_percent['14-18'].summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### By year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\basil\\Anaconda3\\envs\\py2\\lib\\site-packages\\scipy\\stats\\stats.py:1416: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=12\n",
      "  \"anyway, n=%i\" % int(n))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>suicide_rate_per100k_annualized</td> <th>  R-squared:         </th> <td>   0.217</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                          <td>OLS</td>               <th>  Adj. R-squared:    </th> <td>   0.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                    <td>Least Squares</td>          <th>  F-statistic:       </th> <td>   2.774</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                    <td>Mon, 09 Aug 2021</td>         <th>  Prob (F-statistic):</th>  <td> 0.127</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                        <td>15:43:53</td>             <th>  Log-Likelihood:    </th> <td>  11.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>             <td>    12</td>              <th>  AIC:               </th> <td>  -18.23</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>                 <td>    10</td>              <th>  BIC:               </th> <td>  -17.26</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>                     <td>     1</td>              <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>             <td>nonrobust</td>            <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    0.2997</td> <td>    0.035</td> <td>    8.565</td> <td> 0.000</td> <td>    0.222</td> <td>    0.378</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>is_summer</th> <td>   -0.1166</td> <td>    0.070</td> <td>   -1.665</td> <td> 0.127</td> <td>   -0.273</td> <td>    0.039</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 7.710</td> <th>  Durbin-Watson:     </th> <td>   2.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.021</td> <th>  Jarque-Bera (JB):  </th> <td>   3.573</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.178</td> <th>  Prob(JB):          </th> <td>   0.168</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.265</td> <th>  Cond. No.          </th> <td>    2.48</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                   OLS Regression Results                                  \n",
       "===========================================================================================\n",
       "Dep. Variable:     suicide_rate_per100k_annualized   R-squared:                       0.217\n",
       "Model:                                         OLS   Adj. R-squared:                  0.139\n",
       "Method:                              Least Squares   F-statistic:                     2.774\n",
       "Date:                             Mon, 09 Aug 2021   Prob (F-statistic):              0.127\n",
       "Time:                                     15:43:53   Log-Likelihood:                 11.113\n",
       "No. Observations:                               12   AIC:                            -18.23\n",
       "Df Residuals:                                   10   BIC:                            -17.26\n",
       "Df Model:                                        1                                         \n",
       "Covariance Type:                         nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      0.2997      0.035      8.565      0.000       0.222       0.378\n",
       "is_summer     -0.1166      0.070     -1.665      0.127      -0.273       0.039\n",
       "==============================================================================\n",
       "Omnibus:                        7.710   Durbin-Watson:                   2.045\n",
       "Prob(Omnibus):                  0.021   Jarque-Bera (JB):                3.573\n",
       "Skew:                           1.178   Prob(JB):                        0.168\n",
       "Kurtosis:                       4.265   Cond. No.                         2.48\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regs_by_year['<14']['1986'].summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "regs_by_year = {\n",
    "    group: {}\n",
    "    for group in dfs_by_year\n",
    "}\n",
    "pct_regs_by_year = {\n",
    "    group: {}\n",
    "    for group in dfs_by_year\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "for group in regs_by_year:\n",
    "    for year in dfs_by_year[group]:\n",
    "        regs_by_year[group][year] = sm.formula.ols(data=dfs_by_year[group][year], formula=SUMMER_FML).fit()\n",
    "        pct_regs_by_year[group][year] = sm.formula.ols(data=dfs_by_year[group][year], formula=PCT_SUMMER_FML).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_by_year(regs_by_year, func):\n",
    "    df = {\n",
    "        group: {\n",
    "            year: func(regs_by_year[group][year])\n",
    "            for year in regs_by_year[group]\n",
    "        }\n",
    "        for group in regs_by_year\n",
    "    }\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_params = lambda x: x.params['is_summer']\n",
    "get_intercept = lambda x: x.params['Intercept']\n",
    "get_se = lambda x: x.bse['is_summer']\n",
    "get_ci = lambda x: x.conf_int().loc['is_summer']\n",
    "\n",
    "params_by_year = get_by_year(regs_by_year, get_params)\n",
    "params_by_year = pd.DataFrame(params_by_year)[GROUP_ORDER]\n",
    "params_by_year.index = pd.to_datetime(params_by_year.index)\n",
    "\n",
    "intercept_by_year = get_by_year(regs_by_year, get_intercept)\n",
    "intercept_by_year = pd.DataFrame(intercept_by_year)[GROUP_ORDER]\n",
    "intercept_by_year.index = pd.to_datetime(intercept_by_year.index)\n",
    "\n",
    "se_by_year = get_by_year(regs_by_year, get_se)\n",
    "se_by_year = pd.DataFrame(se_by_year)[GROUP_ORDER]\n",
    "se_by_year.index = pd.to_datetime(se_by_year.index)\n",
    "\n",
    "ci_by_year = get_by_year(regs_by_year, get_ci)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params_by_year.plot(figsize=(12, 8), style='-*', color=COLORS)\n",
    "_ = plt.title('Summer effect on suicide rate per 100k' + year_range_str, fontsize=16, weight='heavy')\n",
    "_ = plt.xticks(fontsize=14)\n",
    "_ = plt.xlabel('year', fontsize=14)\n",
    "_ = plt.yticks(fontsize=14)\n",
    "_ = plt.axhline(0.0, color='k')\n",
    "_ = plt.legend(fontsize=14)\n",
    "if SAVE_FIGS:\n",
    "    plt.savefig('figures/summer_coefficient.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "pct_params_by_year = get_by_year(pct_regs_by_year, get_params)\n",
    "pct_params_by_year = pd.DataFrame(pct_params_by_year)[GROUP_ORDER]\n",
    "pct_params_by_year.index = pd.to_datetime(pct_params_by_year.index)\n",
    "\n",
    "pct_se_by_year = get_by_year(pct_regs_by_year, get_se)\n",
    "pct_se_by_year = pd.DataFrame(pct_se_by_year)[GROUP_ORDER]\n",
    "pct_se_by_year.index = pd.to_datetime(pct_se_by_year.index)\n",
    "\n",
    "pct_ci_by_year = get_by_year(pct_regs_by_year, get_ci)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pct_params_by_year.plot(figsize=(12, 8), style='-*', color=COLORS)\n",
    "_ = plt.title('Summer effect (%) on suicide rate per 100k' + year_range_str, fontsize=16, weight='heavy')\n",
    "_ = plt.xticks(fontsize=14)\n",
    "_ = plt.xlabel('year', fontsize=14)\n",
    "_ = plt.yticks(fontsize=14)\n",
    "_ = plt.axhline(0.0, color='k')\n",
    "_ = plt.legend(fontsize=14)\n",
    "if SAVE_FIGS:\n",
    "    plt.savefig('figures/summer_pct_coefficient.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# BOTE calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "summer_rate = suicide_rate_per100k_annualized_by_month.loc[6:8].mean()\n",
    "restofyear_minus_summer = suicide_rate_per100k_annualized_by_month - summer_rate\n",
    "restofyear_minus_summer = restofyear_minus_summer.loc[range(1, 6) + range(9, 13)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pop_latest = pop_by_group.iloc[-1]\n",
    "pop_latest_div_100k = pop_latest/100000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<14        339.936234\n",
       "14-18     2270.116246\n",
       "19-25    -1391.244882\n",
       ">25     -20604.410140\n",
       "dtype: float64"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(restofyear_minus_summer*pop_latest_div_100k).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py2",
   "language": "python",
   "name": "py2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}