FinalProject_group010.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are primarily interested in seeing if a country’s GDP per capita has an impact in the probability and likelihood of a citizen or resident of that country to commit suicide. If a correlation is seen, this information could help prevent the number of suicides committed in certain countries by raising awareness and sharing knowledge of this correlation. Finding the correlation could possibly reduce the impact GDP per capita has on the number of suicides seen in countries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Names\n",
    "- Lucy Xu\n",
    "- Tracy He\n",
    "- Stacy Kim\n",
    "- Thy Vu\n",
    "- Stephanie Schneider\n",
    "- Amaya Mali"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Research Question\n",
    "Is there a correlation between a country’s GDP per capita and the number of suicides committed per population in that country and how has the increase or decrease in GDP affect this rate of suicide over time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work\n",
    "\n",
    "Suicide is a major health problem that happens in almost every country and is the leading cost of death in the United States. Suicide has lasting effects on the family, friends, and the communities of the person committing it. There have been multiple attempts to decrease suicides: emergency suicide hotline, therapy services, etc., but since the reasons behind suicide vary, it is difficult to prevent it indefinitely. We plan to analyze the conditions of countries by observing each countries’ GDP per capita over time to see if there is a correlation between this value and suicide rates. GDP per capita is a measure of a country’s economic output that accounts for its number of people. It is the best measurement of a country’s standard of living and is indicative of the prosperity of a country. Therefore, we have decided to measure the relationship between these two values to foresee any correlation that might be visible and to prevent future rates from increasing by analyzing the rate of increase or decrease in suicide rates in relation to the increase or decrease in GDP per capita over time. There have been research studies done on suicide rates in OECD countries and they found that adolescence and the male gender were both high factors contributing to the risk and that there generally was a difference between rates for both genders. In Brazil, they did a study research suicide rates and economic indicators such as GDP and unemployment. They discovered that there was an inverse relationship between suicide rates and unemployment, however there did not seem to be a relationship to Brazil’s GDP.\n",
    "\n",
    "References (include links):\n",
    "\n",
    "1) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5912485/\n",
    "2) https://www.ingentaconnect.com/content/wk/yco/2018/00000031/00000003/art00015\n",
    "3) https://www.who.int/news-room/fact-sheets/detail/suicide?fbclid=IwAR0YVVPJ6yid-F7u96vg9NXaBfXXPfw_-Hxdw5pOCnaPbbXmox7cuGrJRDM  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis\n",
    "\n",
    "There is a correlation between a country’s GDP per capita and the number of suicides committed per population and we hypothesize that countries with lower GDP per capita will have greater rates of suicide due to poorer socioeconomic conditions and lack of mental health awareness and understanding. However, since various different factors contribute to suicide, we do not predict that the correlation will be very strong."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016\n",
    "We found our data through Kaggle in which the author compiled dataset that was pulled from four datasets– specifically from the United Nations Development Program, World Bank, World Health Organization and, Suicide in the 21st Century (another dataset on Kaggle). The author hoped that the dataset would inspire suicide prevention among those who used it.\n",
    "\n",
    "This dataset provides 27,800 rows of data specifically on the country, year, sex, age, suicides/100k (of population), gdp, and more. We feel that given the large amount of data this dataset provides, we are able to make a reliable inference because it is representative of the countries we will be looking at. \n",
    "\n",
    "The dataset “Suicide Rates Overview 1985 to 2016” includes variables regarding country, year, sex, age, number of suicides, population, suicide per 100k in population, HDI per year, GDP per year and per capita, and generation. It includes 27.8k observations and is a compilation of 4 different datasets that were used to potentially find any correlations between suicide rates in different categories.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import patsy\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from scipy import stats\n",
    "import patsy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('master.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  <font color='salmon'>Testing for missing countries</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Once we recognized that some countries have missing years (not listed as 0 or NaN, but rather, nonexistent rows), we decided to find out which countries were lacking in data. We worked through by range to see which year(s) had the most available countries listed before cleaning so that we can provide the most accurate analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  <font color='purple'>Missing countries within full (1987-2016) range</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101   4   97\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Extracting country-year column into usable variable (the - threw off further code)\n",
    "\"\"\"\n",
    "\n",
    "df.country.unique()\n",
    "df['countryyear'] = df['country-year']\n",
    "\n",
    "\"\"\"\n",
    "adding csv_df to countries variable\n",
    "\"\"\"\n",
    "countries = df.country.unique()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Checking for missing country/year data within full 1985-2016 range. \n",
    "Missing data checking for: Fully unlisted year rows, \n",
    "NOT missing portions such as NaN, None, or 0.\n",
    "\"\"\"\n",
    "years = list(range(1985,2017))\n",
    "missing_data_countries = {}\n",
    "for i in countries:\n",
    "    for j in years:\n",
    "        cntry_yr = str(i+str(j))\n",
    "        if len(df[df.countryyear==cntry_yr]) <= 0:\n",
    "            if i in missing_data_countries:\n",
    "                missing_data_countries[i].append(j)\n",
    "            else:\n",
    "                missing_data_countries[i] = [j]\n",
    "                \n",
    "                \n",
    "\"\"\"\n",
    "Printing out below cell in this order:\n",
    "1 Original number of countries,\n",
    "2 Number with full year(s) data available within range, \n",
    "3 Number of countries without available data from ALL year(s) within range.\n",
    "\"\"\"\n",
    "original = len(df.country.unique())\n",
    "for i in missing_data_countries.keys():\n",
    "    df = df[df.country != i]\n",
    "    \n",
    "removed = len(df.country.unique())\n",
    "\n",
    "print(original,\" \",removed,\" \",len(missing_data_countries))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The above cell showcases that within the full range (1985-2016), only 4 countries reported all years. Now we will try again with smaller ranges, with preference for recent years, excluding 2016 as it a commonly unreported year (16 out of 101 countries reported in 2016 vs 62 out of 101 in 2015)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='purple'>Missing countries within 2000-2015 range</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101   49   52\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Reloading csv setup\n",
    "\"\"\"\n",
    "df = pd.read_csv('master.csv')\n",
    "df.country.unique()\n",
    "df['countryyear'] = df['country-year']\n",
    "countries = df.country.unique()\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Checking for missing country/year data within 2000-2015 range. \n",
    "Missing data checking for: \n",
    "Fully unlisted year rows, NOT missing portions such as NaN, None, or 0.\n",
    "\"\"\"\n",
    "years = list(range(2000,2016))\n",
    "missing_data_countries = {}\n",
    "for i in countries:\n",
    "    for j in years:\n",
    "        cntry_yr = str(i+str(j))\n",
    "        if len(df[df.countryyear==cntry_yr]) <= 0:\n",
    "            if i in missing_data_countries:\n",
    "                missing_data_countries[i].append(j)\n",
    "            else:\n",
    "                missing_data_countries[i] = [j]\n",
    "                \n",
    "\"\"\"\n",
    "Printing out below cell in this order:\n",
    "1 Original number of countries,\n",
    "2 Number with full year(s) data available within range, \n",
    "3 Number of countries without available data from ALL year(s) within range.\n",
    "\"\"\"\n",
    "original = len(df.country.unique())\n",
    "for i in missing_data_countries.keys():\n",
    "    df = df[df.country != i]\n",
    "    \n",
    "removed = len(df.country.unique())\n",
    "\n",
    "print(original,\" \",removed,\" \",len(missing_data_countries))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this stage, over half of available countries did not report all years within range.\n",
    "Further shortening year range below. \n",
    "  Report:\n",
    "    2005-2015 showed 54 out of 101 countries with full years data (47 missing)\n",
    "    2005-2014 showed 63 out of 101 countries with full years data (38 missing)\n",
    "    2005-2013 showed 68 out of 101 countries with full years data (33 missing)\n",
    "    2006-2013 70/101 available (31 missing)\n",
    "    2006-2012 71/101 available (30 missing)\n",
    "    2007-2012 72/101 available (29 missing)\n",
    "    2008-2013 72/101 available (29 missing)\n",
    "    2008-2014 67/101 available (34 missing)\n",
    "    2007-2013 71/101 available (30 missing)\n",
    "    Currently, 2007-2013 is the largest range of available country-year data to work with.\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='purple'>Missing countries within 2000-2015 range</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Reloading csv setup\n",
    "\"\"\"\n",
    "df = pd.read_csv('master.csv')\n",
    "df.country.unique()\n",
    "df['countryyear'] = df['country-year']\n",
    "countries = df.country.unique()\n",
    "\n",
    "\"\"\"\n",
    "Checking for missing country/year data within 2007-2013 range. \n",
    "Missing data checking for: \n",
    "Fully unlisted year rows, NOT missing portions such as NaN, None, or 0.\n",
    "\"\"\"\n",
    "years = list(range(2007,2014))\n",
    "missing_data_countries = {}\n",
    "for i in countries:\n",
    "    for j in years:\n",
    "        cntry_yr = str(i+str(j))\n",
    "        if len(df[df.countryyear==cntry_yr]) <= 0:\n",
    "            if i in missing_data_countries:\n",
    "                missing_data_countries[i].append(j)\n",
    "            else:\n",
    "                missing_data_countries[i] = [j]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Checking for missing country/year data within 2007-2013 range. \n",
    "Missing data checking for: \n",
    "Fully unlisted year rows, NOT missing portions such as NaN, None, or 0.\n",
    "\"\"\"\n",
    "years = list(range(2007,2014))\n",
    "missing_data_countries = {}\n",
    "for i in countries:\n",
    "    for j in years:\n",
    "        cntry_yr = str(i+str(j))\n",
    "        if len(df[df.countryyear==cntry_yr]) <= 0:\n",
    "            if i in missing_data_countries:\n",
    "                missing_data_countries[i].append(j)\n",
    "            else:\n",
    "                missing_data_countries[i] = [j]\n",
    "                \n",
    "\"\"\"\n",
    "Printing out below cell in this order:\n",
    "1 Original number of countries,\n",
    "2 Number with full year(s) data available within range, \n",
    "3 Number of countries without available data from ALL year(s) within range.\n",
    "\"\"\"\n",
    "original = len(df.country.unique())\n",
    "for i in missing_data_countries.keys():\n",
    "    df = df[df.country != i]\n",
    "    \n",
    "removed = len(df.country.unique())\n",
    "\n",
    "print(original,\" \",removed,\" \",len(missing_data_countries))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicides/100k pop</th>\n",
       "      <th>country-year</th>\n",
       "      <th>HDI for year</th>\n",
       "      <th>gdp_for_year ($)</th>\n",
       "      <th>gdp_per_capita ($)</th>\n",
       "      <th>generation</th>\n",
       "      <th>countryyear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>202</td>\n",
       "      <td>363000</td>\n",
       "      <td>55.65</td>\n",
       "      <td>Argentina1985</td>\n",
       "      <td>0.694</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>G.I. Generation</td>\n",
       "      <td>Argentina1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>485</td>\n",
       "      <td>1997000</td>\n",
       "      <td>24.29</td>\n",
       "      <td>Argentina1985</td>\n",
       "      <td>0.694</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>G.I. Generation</td>\n",
       "      <td>Argentina1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>414</td>\n",
       "      <td>3346300</td>\n",
       "      <td>12.37</td>\n",
       "      <td>Argentina1985</td>\n",
       "      <td>0.694</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>Silent</td>\n",
       "      <td>Argentina1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>210</td>\n",
       "      <td>2304000</td>\n",
       "      <td>9.11</td>\n",
       "      <td>Argentina1985</td>\n",
       "      <td>0.694</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>G.I. Generation</td>\n",
       "      <td>Argentina1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>177</td>\n",
       "      <td>2234200</td>\n",
       "      <td>7.92</td>\n",
       "      <td>Argentina1985</td>\n",
       "      <td>0.694</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>Boomers</td>\n",
       "      <td>Argentina1985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year     sex          age  suicides_no  population  \\\n",
       "588  Argentina  1985    male    75+ years          202      363000   \n",
       "589  Argentina  1985    male  55-74 years          485     1997000   \n",
       "590  Argentina  1985    male  35-54 years          414     3346300   \n",
       "591  Argentina  1985  female  55-74 years          210     2304000   \n",
       "592  Argentina  1985    male  25-34 years          177     2234200   \n",
       "\n",
       "     suicides/100k pop   country-year  HDI for year  gdp_for_year ($)   \\\n",
       "588              55.65  Argentina1985         0.694     88,416,668,900   \n",
       "589              24.29  Argentina1985         0.694     88,416,668,900   \n",
       "590              12.37  Argentina1985         0.694     88,416,668,900   \n",
       "591               9.11  Argentina1985         0.694     88,416,668,900   \n",
       "592               7.92  Argentina1985         0.694     88,416,668,900   \n",
       "\n",
       "     gdp_per_capita ($)       generation    countryyear  \n",
       "588                3264  G.I. Generation  Argentina1985  \n",
       "589                3264  G.I. Generation  Argentina1985  \n",
       "590                3264           Silent  Argentina1985  \n",
       "591                3264  G.I. Generation  Argentina1985  \n",
       "592                3264          Boomers  Argentina1985  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <font color='purple'>From the above data testing, we decided to continue with the range of 2007-2013.</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This gives us the rows with missing data and then we will see how many values in each category are NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                   0\n",
       "year                      0\n",
       "sex                       0\n",
       "age                       0\n",
       "suicides_no               0\n",
       "population                0\n",
       "suicides/100k pop         0\n",
       "country-year              0\n",
       "HDI for year          11392\n",
       " gdp_for_year ($)         0\n",
       "gdp_per_capita ($)        0\n",
       "generation                0\n",
       "countryyear               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Gives us the rows with missing data\n",
    "df.isnull()\n",
    "missing_info = df[df.isnull().any(axis = 1)]\n",
    "\n",
    "df.dropna(subset = ['HDI for year', 'country', 'year'])\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicides/100k pop</th>\n",
       "      <th>gdp_for_year ($)</th>\n",
       "      <th>gdp_per_capita ($)</th>\n",
       "      <th>generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>202</td>\n",
       "      <td>363000</td>\n",
       "      <td>55.65</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>485</td>\n",
       "      <td>1997000</td>\n",
       "      <td>24.29</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>414</td>\n",
       "      <td>3346300</td>\n",
       "      <td>12.37</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>Silent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>210</td>\n",
       "      <td>2304000</td>\n",
       "      <td>9.11</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>177</td>\n",
       "      <td>2234200</td>\n",
       "      <td>7.92</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>Boomers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year     sex          age  suicides_no  population  \\\n",
       "588  Argentina  1985    male    75+ years          202      363000   \n",
       "589  Argentina  1985    male  55-74 years          485     1997000   \n",
       "590  Argentina  1985    male  35-54 years          414     3346300   \n",
       "591  Argentina  1985  female  55-74 years          210     2304000   \n",
       "592  Argentina  1985    male  25-34 years          177     2234200   \n",
       "\n",
       "     suicides/100k pop  gdp_for_year ($)   gdp_per_capita ($)       generation  \n",
       "588              55.65     88,416,668,900                3264  G.I. Generation  \n",
       "589              24.29     88,416,668,900                3264  G.I. Generation  \n",
       "590              12.37     88,416,668,900                3264           Silent  \n",
       "591               9.11     88,416,668,900                3264  G.I. Generation  \n",
       "592               7.92     88,416,668,900                3264          Boomers  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removes columns 'HDI for year' and 'country-year'\n",
    "df = df.drop(columns = ['HDI for year', 'country-year', 'countryyear'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicides/100k_pop</th>\n",
       "      <th>gdp_year</th>\n",
       "      <th>gdp_capita</th>\n",
       "      <th>generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>202</td>\n",
       "      <td>363000</td>\n",
       "      <td>55.65</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>485</td>\n",
       "      <td>1997000</td>\n",
       "      <td>24.29</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>414</td>\n",
       "      <td>3346300</td>\n",
       "      <td>12.37</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>Silent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>210</td>\n",
       "      <td>2304000</td>\n",
       "      <td>9.11</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>177</td>\n",
       "      <td>2234200</td>\n",
       "      <td>7.92</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>Boomers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year     sex          age  suicides_no  population  \\\n",
       "588  Argentina  1985    male    75+ years          202      363000   \n",
       "589  Argentina  1985    male  55-74 years          485     1997000   \n",
       "590  Argentina  1985    male  35-54 years          414     3346300   \n",
       "591  Argentina  1985  female  55-74 years          210     2304000   \n",
       "592  Argentina  1985    male  25-34 years          177     2234200   \n",
       "\n",
       "     suicides/100k_pop        gdp_year  gdp_capita       generation  \n",
       "588              55.65  88,416,668,900        3264  G.I. Generation  \n",
       "589              24.29  88,416,668,900        3264  G.I. Generation  \n",
       "590              12.37  88,416,668,900        3264           Silent  \n",
       "591               9.11  88,416,668,900        3264  G.I. Generation  \n",
       "592               7.92  88,416,668,900        3264          Boomers  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = [\"country\", \"year\", \"sex\" ,\"age\", \"suicides_no\", \"population\", \"suicides/100k_pop\",\n",
    "              \"gdp_year\", \"gdp_capita\", \"generation\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='mediumorchid'>2007-2013 DATASET</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicides/100k_pop</th>\n",
       "      <th>gdp_year</th>\n",
       "      <th>gdp_capita</th>\n",
       "      <th>generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2007</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>225</td>\n",
       "      <td>634995</td>\n",
       "      <td>35.43</td>\n",
       "      <td>287,530,508,431</td>\n",
       "      <td>7918</td>\n",
       "      <td>Silent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2007</td>\n",
       "      <td>male</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>577</td>\n",
       "      <td>3381171</td>\n",
       "      <td>17.07</td>\n",
       "      <td>287,530,508,431</td>\n",
       "      <td>7918</td>\n",
       "      <td>Millenials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2007</td>\n",
       "      <td>male</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>438</td>\n",
       "      <td>2582754</td>\n",
       "      <td>16.96</td>\n",
       "      <td>287,530,508,431</td>\n",
       "      <td>7918</td>\n",
       "      <td>Silent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2007</td>\n",
       "      <td>male</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>490</td>\n",
       "      <td>3125322</td>\n",
       "      <td>15.68</td>\n",
       "      <td>287,530,508,431</td>\n",
       "      <td>7918</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2007</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>571</td>\n",
       "      <td>4436541</td>\n",
       "      <td>12.87</td>\n",
       "      <td>287,530,508,431</td>\n",
       "      <td>7918</td>\n",
       "      <td>Boomers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year   sex          age  suicides_no  population  \\\n",
       "852  Argentina  2007  male    75+ years          225      634995   \n",
       "853  Argentina  2007  male  15-24 years          577     3381171   \n",
       "854  Argentina  2007  male  55-74 years          438     2582754   \n",
       "855  Argentina  2007  male  25-34 years          490     3125322   \n",
       "856  Argentina  2007  male  35-54 years          571     4436541   \n",
       "\n",
       "     suicides/100k_pop         gdp_year  gdp_capita    generation  \n",
       "852              35.43  287,530,508,431        7918        Silent  \n",
       "853              17.07  287,530,508,431        7918    Millenials  \n",
       "854              16.96  287,530,508,431        7918        Silent  \n",
       "855              15.68  287,530,508,431        7918  Generation X  \n",
       "856              12.87  287,530,508,431        7918       Boomers  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get values of dataframe from only 2007-2013\n",
    "df_range = df.loc[(df['year'] >= 2007)& (df['year'] <= 2013)]\n",
    "df_range.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:6: 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",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicides/100k_pop</th>\n",
       "      <th>gdp_year</th>\n",
       "      <th>gdp_capita</th>\n",
       "      <th>generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>852</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2007</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>225</td>\n",
       "      <td>634995</td>\n",
       "      <td>35.43</td>\n",
       "      <td>287530508431</td>\n",
       "      <td>7918</td>\n",
       "      <td>Silent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2007</td>\n",
       "      <td>male</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>577</td>\n",
       "      <td>3381171</td>\n",
       "      <td>17.07</td>\n",
       "      <td>287530508431</td>\n",
       "      <td>7918</td>\n",
       "      <td>Millenials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2007</td>\n",
       "      <td>male</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>438</td>\n",
       "      <td>2582754</td>\n",
       "      <td>16.96</td>\n",
       "      <td>287530508431</td>\n",
       "      <td>7918</td>\n",
       "      <td>Silent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2007</td>\n",
       "      <td>male</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>490</td>\n",
       "      <td>3125322</td>\n",
       "      <td>15.68</td>\n",
       "      <td>287530508431</td>\n",
       "      <td>7918</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2007</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>571</td>\n",
       "      <td>4436541</td>\n",
       "      <td>12.87</td>\n",
       "      <td>287530508431</td>\n",
       "      <td>7918</td>\n",
       "      <td>Boomers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year   sex          age  suicides_no  population  \\\n",
       "852  Argentina  2007  male    75+ years          225      634995   \n",
       "853  Argentina  2007  male  15-24 years          577     3381171   \n",
       "854  Argentina  2007  male  55-74 years          438     2582754   \n",
       "855  Argentina  2007  male  25-34 years          490     3125322   \n",
       "856  Argentina  2007  male  35-54 years          571     4436541   \n",
       "\n",
       "     suicides/100k_pop      gdp_year  gdp_capita    generation  \n",
       "852              35.43  287530508431        7918        Silent  \n",
       "853              17.07  287530508431        7918    Millenials  \n",
       "854              16.96  287530508431        7918        Silent  \n",
       "855              15.68  287530508431        7918  Generation X  \n",
       "856              12.87  287530508431        7918       Boomers  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removed the comma in the values in 'gdp_for_year ($)' [2007-2013]\n",
    "gdp_year = []\n",
    "for elem in df_range['gdp_year']:\n",
    "    gdp_year.append(int(elem.replace(',','')))\n",
    "\n",
    "df_range['gdp_year'] = gdp_year\n",
    "df_range.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Viewing all the relationships within the dataset. The below visualization shows that there is some correlation between gdp and number of suicides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1192.48x1080 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\")\n",
    "sns.pairplot(df_range, hue=\"generation\")\n",
    "\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In the above visualization, we wanted to show the relationship between variables as well as the distribution of single variables. To do this we created a pair plot with the variables year, suicides_no, population suicides/100k_pop, gdp_year, and gdp_capita from the dataframe df_range which extracted data from the years 2007 to 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Because the above visualization does show some correlation between GDP and number of suicides, we continue to examine that more in depth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "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>suicides_no</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>20979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>8949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>13891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belize</th>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>67444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           suicides_no\n",
       "country               \n",
       "Argentina        20979\n",
       "Austria           8949\n",
       "Belgium          13891\n",
       "Belize             117\n",
       "Brazil           67444"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creates a groupby object for the sum of # of suicides [2007]\n",
    "gb_sum_suicides_range = df_range.groupby('country')['suicides_no'].sum()\n",
    "\n",
    "\n",
    "#creates a groupby object for the average gdp per year [2007]\n",
    "gb_avg_gdp_year_range = df_range.groupby('country')['gdp_year'].mean()\n",
    "#gb_avg_gdp_year_range.head()\n",
    "\n",
    "\n",
    "#creates a groupby object for the average gdp per capita [2007]\n",
    "gb_avg_gdp_capita_range = df_range.groupby('country')['gdp_capita'].mean()\n",
    "#gb_avg_gdp_capita_range.head()\n",
    "\n",
    "\n",
    "#converts the groupby object back to a dataframe [2007]\n",
    "df_sum_suicides_range = gb_sum_suicides_range.to_frame()\n",
    "df_sum_suicides_range.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "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>gdp_capita</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>11478.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>51597.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>48775.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belize</th>\n",
       "      <td>5063.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>11384.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             gdp_capita\n",
       "country                \n",
       "Argentina  11478.571429\n",
       "Austria    51597.142857\n",
       "Belgium    48775.714286\n",
       "Belize      5063.857143\n",
       "Brazil     11384.000000"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converts the groupby object back to a dataframe [2007]\n",
    "df_avg_gdp_capita_range = gb_avg_gdp_capita_range.to_frame()\n",
    "df_avg_gdp_capita_range.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converts the suicides_no to a list so we can add it to other dataframes [2007]\n",
    "suicides_list = df_sum_suicides_range['suicides_no'].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='salmon'>Table:</font> \n",
    "### <font color='purple'>2007-2013 GDP Per Capita VS. Number of Suicides</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "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>gdp_capita</th>\n",
       "      <th>suicide_no</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>11478.571429</td>\n",
       "      <td>20979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>3628.285714</td>\n",
       "      <td>473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>58256.571429</td>\n",
       "      <td>16907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>51597.142857</td>\n",
       "      <td>8949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahamas</th>\n",
       "      <td>31223.571429</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             gdp_capita  suicide_no\n",
       "country                            \n",
       "Argentina  11478.571429       20979\n",
       "Armenia     3628.285714         473\n",
       "Australia  58256.571429       16907\n",
       "Austria    51597.142857        8949\n",
       "Bahamas    31223.571429          39"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#adds suicide_no to gdp per capita dataframe [2007]\n",
    "df_avg_gdp_capita_range['suicide_no'] = suicides_list\n",
    "df_avg_gdp_capita_range.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='salmon'>Graph:</font> \n",
    "### <font color='purple'>2007-2013 GDP Per Capita VS. Number of Suicides</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3ba1f2e80>"
      ]
     },
     "execution_count": 133,
     "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": [
    "#graph of gdp per capita versus num of suicides [2007]\n",
    "df_avg_gdp_capita_range.plot.scatter(x='gdp_capita', y='suicide_no')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The above visualization is a scatter plot because we wanted to see whether gdp_capita and suicide_no have an effect on each other and up until about 40,000 gdp_capita there seems to be slight trend with a few outliers but after 40,000 there is little to no correlation evident."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='salmon'>Table:</font> \n",
    "### <font color='purple'>2007-2013 GDP Per Year VS. Number of Suicides</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "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>gdp_year</th>\n",
       "      <th>suicide_no</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>4.334090e+11</td>\n",
       "      <td>20979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>1.009421e+10</td>\n",
       "      <td>473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1.212378e+12</td>\n",
       "      <td>16907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>4.116664e+11</td>\n",
       "      <td>8949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahamas</th>\n",
       "      <td>1.037723e+10</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               gdp_year  suicide_no\n",
       "country                            \n",
       "Argentina  4.334090e+11       20979\n",
       "Armenia    1.009421e+10         473\n",
       "Australia  1.212378e+12       16907\n",
       "Austria    4.116664e+11        8949\n",
       "Bahamas    1.037723e+10          39"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#adds suicide_no to gdp per year dataframe [2007]\n",
    "df_avg_gdp_year_range['suicide_no'] = suicides_list\n",
    "df_avg_gdp_year_range.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='salmon'>Graph:</font> \n",
    "### <font color='purple'>2007-2013 GDP Per Year VS. Number of Suicides</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3b9cf8160>"
      ]
     },
     "execution_count": 135,
     "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": [
    "#graph of gdp per year versus num of suicides [2007]\n",
    "df_avg_gdp_year_range.plot.scatter('gdp_year','suicide_no' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Above is another scatter plot that we chose to do to see if there was a correlation between gdp per year (as opposed to capita) and suicides_no and there seems to be a much stronger correlation up until 0.4 outside of the 2 to 3 outliers in the visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Argentina    263102109\n",
       "Armenia       19441954\n",
       "Australia    144952605\n",
       "Austria       55843882\n",
       "Bahamas        2329018\n",
       "Name: population, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creates a groupby object for the sum of population [2007]\n",
    "gb_sum_pop_range = df_range.groupby('country')['population'].sum()\n",
    "#gb_sum_pop_range.head()\n",
    "\n",
    "#converts the groupby object back to a dataframe [2007]\n",
    "df_sum_pop_range = gb_sum_pop_range.to_frame()\n",
    "df_sum_pop_range.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='salmon'>TABLE:</font> \n",
    "### <font color='purple'>2007-2013 Population VS. Number of Suicides</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "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>population</th>\n",
       "      <th>suicide_no</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>263102109</td>\n",
       "      <td>20979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>19441954</td>\n",
       "      <td>473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>144952605</td>\n",
       "      <td>16907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>55843882</td>\n",
       "      <td>8949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahamas</th>\n",
       "      <td>2329018</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           population  suicide_no\n",
       "country                          \n",
       "Argentina   263102109       20979\n",
       "Armenia      19441954         473\n",
       "Australia   144952605       16907\n",
       "Austria      55843882        8949\n",
       "Bahamas       2329018          39"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#adds suicide_no to gdp per capita dataframe [2007]\n",
    "df_sum_pop_range['suicide_no'] = suicides_list\n",
    "df_sum_pop_range.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='salmon'>Graph:</font> \n",
    "### <font color='purple'>2007-2013 Population VS. Number of Suicides</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f99ec73a048>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DFwFrI2JnRHQBa4EFad6xEfGTNMbbrf3WNdA2bBCrN21l/nXreO9NG5h/3TrWbNpa75DMrMFUknD+Z0Q8L+l84LfIWi8jGS36pIh4GiA9n5jKpwNbcvU6U9lQ5Z0DlA+1DRvAju4elq1qZ19vH3t69rOvt4+lq9rd0jGzqqok4RxIz28D/iEiVgNHVTEWDVAWIyivbKPSEkltktq2b99e6eINobNrL81NB38Umpua6OzaW6eIzKwRVZJwtkr6R+DdwPcltVS4fMkz6XAY6fnZVN4JzMzVmwFsG6Z8xgDlQ23jEBFxY0S0RkTrtGnTRvB2jnwzJk+kt6/voLLevj5mTJ5Yp4jMrBFVkjDeDdwFLIiIXcAJwMdHsM01QKmn2WJgda78fam32nnA7nQ47C7gQkmTU2eBC4G70rw96U6kAt7Xb10DbcMGMGVSC8sXzWVCcxPHtIxnQnMTyxfNZcqklnqHZmYNRLl7qlV/5dJtwAXAVOAZst5m3wHuAE4BngLeFRE7U9L4IllPsxeA90dEW1rPHwCfTKv9y4j4p1TeSnYuaSJwJ/DhiAhJUwbaxnDxtra2Rlvb2B0ybkd3D51de5kxeaKTjZmVTdLGiGgdtl4tE86RZqwnHDOzkSg34YzkHIyZmVnFnHDMzKwQTjhmZlYIJxwzMyuEE46ZmRXCCcfMzArhhGNmZoVwwjEzs0I44ZiZWSGccMzMrBBOOGZmVggnHDMzK4QTjpmZFcIJx0ZsR3cPm7fs8q2ozaws4+sdgB2ZVm/ayrJV7TQ3NdHb18fyRXO5dN70eodlZqOYWzhWsR3dPSxb1c6+3j729OxnX28fS1e1V6Wl41aTWeNyC8cq1tm1l+amJvbR91JZc1MTnV17D+tOoW41HZl8p1grlxOOVWzG5In09vUdVNbb18eMyRNHvM58q6mUyJauamf+6VP9JTaK+UeCVcKH1KxiUya1sHzRXCY0N3FMy3gmNDexfNHcw0oMpVZTXqnVZKNTLQ+tWmNyC8dG5NJ505l/+tSqHUqpRavJaqtWh1atcbmFYyM2ZVILZ848vipfLrVoNVlt+UeCVcotHBs1qt1qstoq/UhY2u8cjv9uNhgnHBtVpkxq8RfWEcQ/EqwSTjhmdlj8I8HK5XM4ZmZWCCccMzMrhBOOmZkVwgnHzMwKUbeEI+lJSQ9I2iSpLZWdIGmtpMfS8+RULknXS+qQ1C7prNx6Fqf6j0lanCs/O62/Iy2r4t+lmZmV1LuF8+aImBcRren1NcDdETEbuDu9BrgYmJ0eS4AbIEtQwLXAucA5wLWlJJXqLMktt6D2b8fMzAZT74TT30JgRZpeAVyWK781MvcCx0s6GbgIWBsROyOiC1gLLEjzjo2In0REALfm1mVmZnVQz4QTwL9K2ihpSSo7KSKeBkjPJ6by6cCW3LKdqWyo8s4Bys3MrE7qeeHn/IjYJulEYK2k/xyi7kDnX2IE5YeuOEt2SwBOOeWUoSM2M7MRq1sLJyK2pedngW+TnYN5Jh0OIz0/m6p3AjNzi88Atg1TPmOA8oHiuDEiWiOiddq0aYf7tszMbBB1STiSjpZ0TGkauBB4EFgDlHqaLQZWp+k1wPtSb7XzgN3pkNtdwIWSJqfOAhcCd6V5eySdl3qnvS+3rjHDt2s2s9GkXofUTgK+nXoqjwf+OSL+RdJ9wB2SrgSeAt6V6n8fuAToAF4A3g8QETslfRa4L9X7TETsTNMfBG4BJgJ3pseY4Tsxmtloo6wTlwG0trZGW1tbvcM4bDu6e5h/3Tr29b58r5IJzU3cs+wtHmTRzKpO0sbc5S2DGm3doq0KfLtmMxuNnHAakO/EaGajkRNOA/Ltms1sNPIN2BqU78RYvh3dPd5PZgVwwmlgvhPj8Nybz6w4PqRmY9aO7h6WrWpnX28fe3r2s6+3j6Wr2n3dklmNOOHYmOXefGbFcsKxETvSRzJwbz6zYvkcjlVsR3cPX9/wFF9a38FR447ccx+l3nxL+53D8Xkvs9pwwmkgRfS2Wr1pK0tXbqZnfzZCRc/+rIXw8ZXtzD996hH3Ze3efGbFccKps2oliSJ6W5VOspeSTV7P/j7+ecNTfPits6u6zSK4N59ZMXwOp45Wb9rK/OvW8d6bNjD/unWs2bR1ROsZaW+rSs/BDHSSPe+L6x87Ys/nmFntuYVTJ/kksY/ssNTSVSM7LFVKBKX1wMu9rQZb10haRAOdZM87aty4IbdpZmObWzh1Us0uucP1turfkhlpiyg/ZM7RR407ZL57eJnZUNzCqZNqdskdqrfVQC2ZV005uuIWUUn+JPuD23bz2e/93D28zKwsTjh1Uu0uuQP1thrssN33rjr/sJJd6ST7mTOPZ8EZv+IeXmZWFiecOhqqS+6O7h4e2rYbEGe88tiyvsz797Ya7NzOL188ULVk5x5eZlYuJ5wCDdQFeqAv7NWbtvKn39xM74Gs+/H4Jvj8u+dV3M15qMN2Z8483tefmFmh3GmgIOV2gd7R3cPSle0vJRuA/X3w8ZWbK+5yPNh9cQA2b9kFwJkzj3eyMbNCuIVTgIHOpfS/Mr/U+tm990XGNemQdYxTeSf1++t/2O4/Op5j/nXrPBy/mRXOCacAA51LyV+Zn+9J9uKBA/QdeiE/B2LkXY5Lh+2qee2PmVmlfEitADMmT+TFA4deMPnF9Y/R8cyeg66J6dkfRATN415u5Yxvgr9555mHnRQ8HL+Z1ZNbODVW6m02d/qx3PeLXQfPDNi0ZRfRr0kzTuIri38tq1BBL7XheDh+M6snJ5wa6t/brL+eA8HkVzTT029+z4HglcdN4PSTjqlqPB6O38zqyQmnykon/3v3H+DjQyQbyI5nbu7cTcv4ppeG+QeY0JxdK1MLHo7fzOrFCaeKSif/IzgogQymD7jpR/814HD/tTzM5Ys1zawenHCqJN8DrBIv9L6cbI5uGceBvvBhLjNrSE44VdLZtfewuvwdfdQ4/vztZ/Dm151Yl2RTxN1CzWxsa+iEI2kB8H+AccBNEfG5WmxnR3cPn/nuQ7xQYesm70BE3ZJNEXcLNTNr2OtwJI0DvgRcDMwBrpA0p9rbWb1pK2f/xb+x8aldw1cewp+9bU7dWjYjuTeOmVmlGjbhAOcAHRHxeES8CNwOLKzmBnZ093D17ZsOez1Ht4zj9dOPq0JElfPFoGZWlEZOONOBLbnXnamsaj675sGqrOdAX9Tt4ktfDGpmRWnkhHPoCJjZpfsHV5KWSGqT1LZ9+/aKNvDjJ3ZWVP/drdOZ0NxESxq2ZkJz00sjONfrRP1gI0q744CZVVsjdxroBGbmXs8AtvWvFBE3AjcCtLa2Dn6V5gAWzj2Zr9zzi2HrjRN85rLX855zX8WyBb9KZ9dejj5qHL988cCo6BXmi0HNrAiNnHDuA2ZLOhXYClwO/G41N/Cpt7+er97zCwbrmzZOsOQ3T+MPf+O0IW+4NhqM1rjMrHE0bMKJiP2SrgLuIusWfXNEPFTt7Tz+ubfxl999kO9seprTTnwF75g3g0kTmjl24njOeOVx/hI3M0sUUdFRpIbW2toabW1t9Q7DzOyIImljRLQOV6+ROw2Ymdko4oRjZmaFcMIxM7NCOOGYmVkhnHDMzKwQ7qWWI2k7MPyVnAObCjxXxXCqxXGVbzTGBI6rUqMxrtEYE1QvrldFxLThKjnhVImktnK6BRbNcZVvNMYEjqtSozGu0RgTFB+XD6mZmVkhnHDMzKwQTjjVc2O9AxiE4yrfaIwJHFelRmNcozEmKDgun8MxM7NCuIVjZmaFcMIpg6QFkh6R1CHpmgHmt0j6Rpq/QdKs3LxPpPJHJF1UYEwfk/RzSe2S7pb0qty8A5I2pceaasVUZly/L2l7bvt/mJu3WNJj6bG44Li+kIvpUUm7cvNqsr8k3SzpWUkD3jpWmetTzO2SzsrNq+W+Gi6u96R42iX9WNKZuXlPSnog7auqjoRbRlwXSNqd+1v9r9y8If/+NYzp47l4HkyfpRPSvFruq5mS1kt6WNJDkq4eoE7xn6+I8GOIB9mtDf4LOA04CtgMzOlX538A/5CmLwe+kabnpPotwKlpPeMKiunNwCvS9AdLMaXX3XXcV78PfHGAZU8AHk/Pk9P05KLi6lf/w2S3s6j1/vpN4CzgwUHmXwLcSXb32vOADbXeV2XG9cbS9oCLS3Gl108CU+u0vy4Avne4f/9qxtSv7tuBdQXtq5OBs9L0McCjA/wvFv75cgtneOcAHRHxeES8CNwOLOxXZyGwIk2vBN4qSan89ojoiYgngI60vprHFBHrI+KF9PJesjue1lo5+2owFwFrI2JnRHQBa4EFdYrrCuC2Km17UBHxQ2Co+5QvBG6NzL3A8ZJOprb7ati4IuLHabtQ3GernP01mMP5XFYzpkI+VwAR8XRE/CxN7wEeBqb3q1b458sJZ3jTgS25150c+od7qU5E7Ad2A1PKXLZWMeVdSfZLpmSCpDZJ90q6rArxVBrXotSEXympdBvwWu2ritadDj2eCqzLFddqfw1nsLhrua8q1f+zFcC/StooaUkd4vl1SZsl3SnpjFRW9/0l6RVkX9qrcsWF7Ctlh/jfAGzoN6vwz1fD3vGzijRAWf+ufYPVKWfZkSh7vZLeC7QCb8oVnxIR2ySdBqyT9EBE/FdBcX0XuC0ieiR9gKxl+JYyl61lXCWXAysj4kCurFb7azhFf64qIunNZAnn/Fzx/LSvTgTWSvrP1Aoows/IhljplnQJ8B1gNqNjf70duCci8q2hmu8rSZPIktxHI+L5/rMHWKSmny+3cIbXCczMvZ4BbBusjqTxwHFkzexylq1VTEj678CngEsjoqdUHhHb0vPjwA/Ifv1Uw7BxRcSOXCxfAc4ud9laxpVzOf0Oe9Rwfw1nsLhrua/KImkucBOwMCJ2lMpz++pZ4NtU5xByWSLi+YjoTtPfB5olTWUU7C+G/lzVZF9JaiZLNl+PiG8NUKX4z1ctTlg10oOsFfg42WGW0gnHM/rV+RAHdxq4I02fwcGdBh6nOp0GyonpDWQnSmf3K58MtKTpqcBjVO8EajlxnZybfgdwb5o+AXgixTc5TZ9QVFyp3mvJTuSqiP2V1jmLwU+Cv42DT+r+tNb7qsy4TiE7H/nGfuVHA8fkpn8MLCgwrl8p/e3IvryfSvuurL9/LWJK80s/QI8ual+l930r8HdD1Cn881W1D0IjP8h6czxK9gX+qVT2GbKWA8AE4Jvpn/CnwGm5ZT+VlnsEuLjAmP4NeAbYlB5rUvkbgQfSP90DwJUF76u/Bh5K218PvC637B+kfdgBvL/IuNLrTwOf67dczfYX2S/ep4Fesl+VVwIfAD6Q5gv4Uor5AaC1oH01XFw3AV25z1ZbKj8t7afN6W/8qYLjuir32bqXXEIc6O9fREypzu+TdR7KL1frfXU+2WGw9tzf6ZJ6f7480oCZmRXC53DMzKwQTjhmZlYIJxwzMyuEE46ZmRXCCcfMbIwabvDRfnVfpWwg4HZJP5BU8ZBGTjhmo5ykWcN9IaQ6v5t73Srp+tpHZ0e4Wyh/nLT/TTb22lyySwr+utKNOeGYNYZZwEsJJyLaIuIj9QvHjgQxwOCjkl4t6V/SGG8/kvS6NGsOcHeaXs8IBkB1wjE7TKl18Z+SVuQGJX2FpLdKuj/d8+RmSS2p/pOSrpP00/Q4PZXfIumdufV2D7KtH0n6WXq8Mc36HPAb6d4qf5zuDfO9tMwJkr6TYrs3DUuDpE+nuH4g6XFJTlAG2W2nPxwRZwN/Cnw5lW8GFqXpdwDHSJpSyYqdcMyq47XAjelww/PAx8gOV/xORPw3suFVPpir/3xEnAN8Efi7CrbzLPBbEXEW8DtA6bDZNcCPImJeRHyh3zJ/DtyfYvsk2ZAnJa8jG47+HODaNP6WjVFpsM83At+UtAn4R7J760CWfN4k6X6ywYC3AvsrWb9Hizarji0RcU+a/r/AnwFPRMSjqWwF2Zh7peRyW+65f4IYSjPwRUnzgAPAa8pY5nzSL9OIWCdpiqTj0rz/F9lgqj2SngVOIhuixcamJmBXRMzrPyOywUZ/G15KTIsiYnelKzezw1fpGFExwPR+0v9kuoHfUQMs98dkY+SdSXYVViDwAAABTUlEQVTbiYHq9DfUcPM9ubID+EfomBbZLQyekPQueOk21Gem6amSSjnjE8DNla7fCcesOk6R9Otp+gqywVNnlc7PAL8H/Huu/u/knn+Spp/k5ds1LCRrzfR3HPB0RPSldY5L5XvIbiU8kB8C7wGQdAHwXBx6bxQbgyTdRvb5e62kTklXkn1WrpRUGli01DngAuARSY+StYT/stLt+deMWXU8DCyW9I9ktzC4mmzE4m+meyTdB/xDrn6LpA1kP/quSGVfAVZL+ilZb6BfDrCdLwOr0i/Q9bk67cD+9CVxC3B/bplPA/8kqR14AVh8eG/VGkVEXDHIrEO6SkfESmDl4WzPo0WbHaZ0C9/vRcTry6z/JNlQ8M/VMCyzUceH1MzMrBBu4ZiZWSHcwjEzs0I44ZiZWSGccMzMrBBOOGZmVggnHDMzK4QTjpmZFeL/A+6nYH9CtJFyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#graph of population versus num of suicides [2007]\n",
    "df_sum_pop_range.plot.scatter('population','suicide_no' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Again, this is a scatter plot comparing population against suicide_no and there also seems to be a strong correlation up until about the 1.00 mark. There are a few outliers as well but there does seem to be a trend."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color='mediumorchid'>GDP 2007-2013 GDP Capita Range </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "814 122729 121915\n"
     ]
    }
   ],
   "source": [
    "#get values of dataframe from only 2008\n",
    "#df_2008 = df.loc[(df['year'] == 2008)]\n",
    "#df_2008.head()\n",
    "r_gdp = df_range['gdp_capita'].max()-df_range['gdp_capita'].min()\n",
    "\n",
    "r_gdp_min = df_range['gdp_capita'].min()\n",
    " \n",
    "r_gdp_max = df_range['gdp_capita'].max()\n",
    "\n",
    "print(r_gdp_min, r_gdp_max, r_gdp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_range_min = df.loc[(df['gdp_capita'] >= 814) & (df['gdp_capita'] <= 61771)]\n",
    "df_range_max = df.loc[(df['gdp_capita'] >= 61772) & (df['gdp_capita'] <= 122729)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicides/100k_pop</th>\n",
       "      <th>gdp_year</th>\n",
       "      <th>gdp_capita</th>\n",
       "      <th>generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>202</td>\n",
       "      <td>363000</td>\n",
       "      <td>55.65</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>485</td>\n",
       "      <td>1997000</td>\n",
       "      <td>24.29</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>414</td>\n",
       "      <td>3346300</td>\n",
       "      <td>12.37</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>Silent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>female</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>210</td>\n",
       "      <td>2304000</td>\n",
       "      <td>9.11</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>177</td>\n",
       "      <td>2234200</td>\n",
       "      <td>7.92</td>\n",
       "      <td>88,416,668,900</td>\n",
       "      <td>3264</td>\n",
       "      <td>Boomers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year     sex          age  suicides_no  population  \\\n",
       "588  Argentina  1985    male    75+ years          202      363000   \n",
       "589  Argentina  1985    male  55-74 years          485     1997000   \n",
       "590  Argentina  1985    male  35-54 years          414     3346300   \n",
       "591  Argentina  1985  female  55-74 years          210     2304000   \n",
       "592  Argentina  1985    male  25-34 years          177     2234200   \n",
       "\n",
       "     suicides/100k_pop        gdp_year  gdp_capita       generation  \n",
       "588              55.65  88,416,668,900        3264  G.I. Generation  \n",
       "589              24.29  88,416,668,900        3264  G.I. Generation  \n",
       "590              12.37  88,416,668,900        3264           Silent  \n",
       "591               9.11  88,416,668,900        3264  G.I. Generation  \n",
       "592               7.92  88,416,668,900        3264          Boomers  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_range_min.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>suicides_no</th>\n",
       "      <th>population</th>\n",
       "      <th>suicides/100k_pop</th>\n",
       "      <th>gdp_year</th>\n",
       "      <th>gdp_capita</th>\n",
       "      <th>generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1726</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2011</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>146</td>\n",
       "      <td>588053</td>\n",
       "      <td>24.83</td>\n",
       "      <td>1,394,280,784,778</td>\n",
       "      <td>66770</td>\n",
       "      <td>Silent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1727</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2011</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>704</td>\n",
       "      <td>3072726</td>\n",
       "      <td>22.91</td>\n",
       "      <td>1,394,280,784,778</td>\n",
       "      <td>66770</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1728</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2011</td>\n",
       "      <td>male</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>347</td>\n",
       "      <td>1610295</td>\n",
       "      <td>21.55</td>\n",
       "      <td>1,394,280,784,778</td>\n",
       "      <td>66770</td>\n",
       "      <td>Millenials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1729</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2011</td>\n",
       "      <td>male</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>366</td>\n",
       "      <td>2104816</td>\n",
       "      <td>17.39</td>\n",
       "      <td>1,394,280,784,778</td>\n",
       "      <td>66770</td>\n",
       "      <td>Boomers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1730</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2011</td>\n",
       "      <td>male</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>242</td>\n",
       "      <td>1570069</td>\n",
       "      <td>15.41</td>\n",
       "      <td>1,394,280,784,778</td>\n",
       "      <td>66770</td>\n",
       "      <td>Millenials</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  year   sex          age  suicides_no  population  \\\n",
       "1726  Australia  2011  male    75+ years          146      588053   \n",
       "1727  Australia  2011  male  35-54 years          704     3072726   \n",
       "1728  Australia  2011  male  25-34 years          347     1610295   \n",
       "1729  Australia  2011  male  55-74 years          366     2104816   \n",
       "1730  Australia  2011  male  15-24 years          242     1570069   \n",
       "\n",
       "      suicides/100k_pop           gdp_year  gdp_capita    generation  \n",
       "1726              24.83  1,394,280,784,778       66770        Silent  \n",
       "1727              22.91  1,394,280,784,778       66770  Generation X  \n",
       "1728              21.55  1,394,280,784,778       66770    Millenials  \n",
       "1729              17.39  1,394,280,784,778       66770       Boomers  \n",
       "1730              15.41  1,394,280,784,778       66770    Millenials  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_range_max.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3b9cac160>"
      ]
     },
     "execution_count": 144,
     "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": [
    "df_range_min.plot.hist(x = 'gdp_capita',y = 'suicides_no' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This histogram was made to show the frequency of number of suicides within ranges of gdp_capita from the dataframe df_range_min, which contains a range of gdp per capita from 814 and 61771. It is a right skewed distribution and it shows a higher frequency of suicides between 0 and 2500 gdp per capita."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3b9c029b0>"
      ]
     },
     "execution_count": 145,
     "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": [
    "df_range_max.plot.hist( x = 'gdp_capita', y = 'suicides_no')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This histogram compares the same variables but from the dataframe df_range_max which contains gdp_capita from 61772 to 122729. It is also a right skewed distribution and shows a higher frequency of suicides from the range 0-200."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='black'>Data Analysis</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='salmon'> T-Test</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.95498703533e-11 There is a significant difference between the suicide numbers!\n"
     ]
    }
   ],
   "source": [
    "\n",
    "t_val,p_val = stats.ttest_ind(df_max_suic,df_min_suic)\n",
    "\n",
    "#Using an alpha value 0.01\n",
    "if p_val < 0.01:\n",
    "    print(p_val,'There is a significant difference between the suicide numbers!')\n",
    "else:\n",
    "    print(p_val,'There is NOT a significant difference between the suicide numbers!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='salmon'> Pivot Table</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "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>suicides_no</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th>gdp_capita</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Argentina</th>\n",
       "      <th>7918</th>\n",
       "      <td>245.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8961</th>\n",
       "      <td>240.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9843</th>\n",
       "      <td>254.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11273</th>\n",
       "      <td>245.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13946</th>\n",
       "      <td>242.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      suicides_no\n",
       "country   gdp_capita             \n",
       "Argentina 7918         245.500000\n",
       "          8961         240.333333\n",
       "          9843         254.916667\n",
       "          11273        245.250000\n",
       "          13946        242.666667"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv = pd.pivot_table(df_range, values = 'suicides_no', index=[ 'country','gdp_capita'])\n",
    "pv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='salmon'>Linear Regression</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            suicides_no   R-squared:                       0.002\n",
      "Model:                            OLS   Adj. R-squared:                  0.002\n",
      "Method:                 Least Squares   F-statistic:                     13.45\n",
      "Date:                Tue, 11 Jun 2019   Prob (F-statistic):           0.000248\n",
      "Time:                        12:56:48   Log-Likelihood:                -48888.\n",
      "No. Observations:                5964   AIC:                         9.778e+04\n",
      "Df Residuals:                    5962   BIC:                         9.779e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    210.1958     17.132     12.269      0.000     176.611     243.780\n",
      "gdp_capita     0.0017      0.000      3.667      0.000       0.001       0.003\n",
      "==============================================================================\n",
      "Omnibus:                     7978.186   Durbin-Watson:                   0.550\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1485159.265\n",
      "Skew:                           7.716   Prob(JB):                         0.00\n",
      "Kurtosis:                      78.752   Cond. No.                     5.69e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 5.69e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "outcome_1_max, predictors_1_max = patsy.dmatrices('suicides_no ~ gdp_capita',df_range)\n",
    "mod_1_max = sm.OLS(outcome_1_max, predictors_1_max)\n",
    "res_1_max = mod_1_max.fit()\n",
    "print(res_1_max.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the R-squared value and the Adjusted R-squared value was 0.002, we can conclude that there was no correlation found between the `suicides_no` amount and the `gdp_capita` variables \n",
    "\n",
    "also with the warnings we also bring into considerations that because the condition number is large there were some numerical problems during the analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis \n",
    "   Firstly, we made 3 scatter plot graphs with different variables each. The first graph relates `gdp_capita` vs. `suicides_no`. The second shows GDP per year vs number of suicides and the third shows population vs. number of suicides. All graphs show data from a six year range (2007 to 2013). Upon looking at the distribution of the spreads, we find that the scatterplots, GDP per year vs. number of suicides and population vs. number of suicides are weakly positively correlated but generally have no correlation since the points seem to be clustered together towards the left corner of the graph. This indicates that countries with small amounts of GDP per year and population also have relatively small numbers of suicide (for both graphs). However, there are still outliers with high GPA per year/population and higher numbers of suicide. In the third graph, GDP per capita vs. the number of suicides, also shows no correlation but more variation among the countries and their GDP per capita. The plot points seem to spread towards the bottom of the graph showing that most countries who have either a low or high GDP per capita have relatively the same small number of suicides. This graph also contains outliers with high number of suicides and low GDP per capita, which shows that although our hypothesis is not supported by this graph, the outliers help show that there are a few countries who have higher suicide rates and low GDP per capita. \n",
    "   \n",
    "   \n",
    "In addition to the scatter plots, we also wanted to make two bar graphs and compare them to see if low GDP and high GDP values for each country have different amounts of suicides. Based on looking at the bar graphs, we found that there was a higher frequency of suicides for countries with low GDP when compared to countries with high GDP. We used a t-test with an alpha level of .01 and found that there was a significant difference between the suicide numbers.\n",
    "\n",
    "To see if this statistically significant from the t-test is related to the correlation between `suicides_no` and `gdp_capita` variables, we did a linear regression model. The r-square value from the regression model also shows that there are no extreme values in the distribution because it was the same value of the adjusted r-square value.  The regression model also shows with the r-square value is very low (0.002) which tells us that only 2% of the variance of GDP per capita is explained by the correlation. The other 98% of the total variation in GDP per capita remains unexplained. This low r-square value suggests that these variables are a 'bad fit' in this model and thus, have no correlation. This could be cause by large condition number (5.69e+04) which indicates that there are strong multicollinearity or other numerical problems. Another explanation might be that the standard error assumes that the covariance matrix of the errors is correctly specified. Due to these warnings, we cannot completely reject the null hypothesis that there is a significant difference between GDP per capita and number of suicides."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ethics & Privacy\n",
    "\n",
    "### <font color='salmon'> A. Data Collection</font>\n",
    "We are using data that is public. All the datasets that we will find are open to the public and found on www.kaggle.com. Due to the public availability of this website, we are not violating the consent of those who gave their data to Kaggle or the author. There are also no ethics-related issues regarding our data as we are simply gathering data and statistics that are present.\n",
    "\n",
    "### <font color='salmon'> B. Data Storage</font>\n",
    "The data presented by Kaggle shows mere overall population demographics with numbers rather than specific names or any traceable ID. Therefore, we are sure that this dataset does not have any risks of privacy violations. \n",
    "\n",
    "### <font color='salmon'> C. Analysis: </font>\n",
    "It is noted that Asian and African countries aren’t included. We are unsure of the reason but have exercised appropriate data cleaning. After the cleaning, our data still included enough variety of GDP in other countries to ensure the validity of the research.\n",
    "\n",
    "In addition, since suicide is still considered a sensitive issue and is highly illegal in some countries, some datas may have been unreported or falsified. According to an article by World Health Organization mentioned in the References, only 60 states in the United States reported \"good-quality\" data on their suicide numbers. This leads to the data reported to not be fully accurate and/or underreported. \n",
    "\n",
    "### <font color='salmon'> D. Modelling</font>\n",
    "As mentioned in part A, the data contains all factual numbers of countries from different parts of the world. There should not be any discriminatory within the data collected or any of our analysis since we are analyzing numbers of facts from a public dataset.\n",
    "\n",
    "### <font color='salmon'> E. Deploy: </font>\n",
    "Despite the fact that our research question relates to a sensitive issue, the research has a scientific and economic aim. All the information is used with consent, therefore not harming any participants with our findings.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion & Discussion \n",
    "\n",
    "In this project we wanted to answer the main question about whether there is a correlation between a country’s `GDP per capita` and the `number of suicides` committed per population in that country. From the analysis, our regression model detected no correlation between the `number of suicides` and `gdp per capita` value. The results concluding no correlation were: the R-square value was very low (0.002) which tells us the `suicides_no` and `gdp_capita` variables do not have a 'good fit' thus have no correlation. Another factor was in the OLS regression results, we were presented with a warning that explained there are possible numerical problems due to the large conditional values. While the t-test in our analysis showed a statistically significant value when comparing the `number of suicides` from the `df_range_min` and then the `number of suicides` from the `df_range_max`, there could've been outside factors the t-test was not able to count for. Additionally, our scatter plots had only shown weak to no correlation between GDP per capita, GDP per year, and population when compared to the number of suicides. \n",
    "\n",
    "We encounter multiple obstacles when finding our data. The largest obstacle that we encountered was when we were cleaning our data. We had to reclean our data because we found that besides NaN values there were also missing entries of years or other information for countries. We realized that trying to replace all those values with averages could also effect our analysis and results because there were too many countries with missing entries and this could effect our sampling distribution and the variance. Primarily, we decided to compare the two years with the least missing entries but this would not give us a statistically correct answer on if `gdp_capita` impacts `suicides_no` for there are other years that can impact the trend. So we decided to find a specific year range that most countries had reported on Kaggle. While over half of available countries did not report all years within the range of 2007 to 2013, this range 2007-2013 was decided on because it had the least amount of missing data and the years were still recent compared to testing years in the 1980s. Because of this, our data is heavily skewed to the countries that we were restricted to. Another obstacle we found was trying to pick the correct tests to analyze our data. Our data had multiple different values so we couldn't clean and use unique values. Also, the `df` contained both interval and ordinal values which also limited what tests we were able to use and how to test our data. This is why we chose to do a t-test and a linear regression model which was summarized by the pivot table. \n",
    "\n",
    "While we found that there were no correlations or significant differences in our hypothesis, we hoped that if our research were correct, we would have been able to find a possible cause for the amount of suicides happening globally. The ability to predict a probable cause would have helped many countries see that the country’s overall wealth is a factor that they could improve on in order to maintain the health and wellbeing of their population and lower possible suicides."
   ]
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   "cell_type": "code",
   "execution_count": null,
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