Suicide_Rates_Analysis_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8a446e0a",
   "metadata": {},
   "source": [
    "# Exploring Suicide Rates and Factors Over 1990 - 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c550b0",
   "metadata": {},
   "source": [
    "## Collaborators: \n",
    "- Dalya Lami\n",
    "- Jahn Ferdinandus\n",
    "- Ali Alam\n",
    "- Dayana Imanova\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6ebdd332",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies and Setup\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import requests\n",
    "import os\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "717d617b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>21.0</td>\n",
       "      <td>312900</td>\n",
       "      <td>6.71</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,15,66,24,900</td>\n",
       "      <td>796.0</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>16.0</td>\n",
       "      <td>308000</td>\n",
       "      <td>5.19</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,15,66,24,900</td>\n",
       "      <td>796.0</td>\n",
       "      <td>Silent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>female</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>14.0</td>\n",
       "      <td>289700</td>\n",
       "      <td>4.83</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,15,66,24,900</td>\n",
       "      <td>796.0</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21800</td>\n",
       "      <td>4.59</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,15,66,24,900</td>\n",
       "      <td>796.0</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>9.0</td>\n",
       "      <td>274300</td>\n",
       "      <td>3.28</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,15,66,24,900</td>\n",
       "      <td>796.0</td>\n",
       "      <td>Boomers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year     sex          age  suicides_no  population  \\\n",
       "0  Albania  1987    male  15-24 years         21.0      312900   \n",
       "1  Albania  1987    male  35-54 years         16.0      308000   \n",
       "2  Albania  1987  female  15-24 years         14.0      289700   \n",
       "3  Albania  1987    male    75+ years          1.0       21800   \n",
       "4  Albania  1987    male  25-34 years          9.0      274300   \n",
       "\n",
       "   suicides/100k pop country-year  HDI for year  gdp_for_year ($)   \\\n",
       "0               6.71  Albania1987           NaN     2,15,66,24,900   \n",
       "1               5.19  Albania1987           NaN     2,15,66,24,900   \n",
       "2               4.83  Albania1987           NaN     2,15,66,24,900   \n",
       "3               4.59  Albania1987           NaN     2,15,66,24,900   \n",
       "4               3.28  Albania1987           NaN     2,15,66,24,900   \n",
       "\n",
       "   gdp_per_capita ($)       generation  \n",
       "0               796.0     Generation X  \n",
       "1               796.0           Silent  \n",
       "2               796.0     Generation X  \n",
       "3               796.0  G.I. Generation  \n",
       "4               796.0          Boomers  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# File to Load\n",
    "suicide_rates_1985_2021 = Path(\"Resources/suicide_rates_1985-2021.csv\")\n",
    "\n",
    "suicide_rates_df = pd.read_csv(suicide_rates_1985_2021)\n",
    "suicide_rates_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0b261830",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'year', 'sex', 'age', 'suicides_no', 'population',\n",
       "       'suicides/100k pop', 'country-year', 'HDI for year',\n",
       "       ' gdp_for_year ($) ', 'gdp_per_capita ($)', 'generation'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of columns\n",
    "suicide_rates_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9cfc48a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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>gdp_for_year ($)</th>\n",
       "      <th>gdp_per_capita ($)</th>\n",
       "      <th>generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>21.0</td>\n",
       "      <td>312900</td>\n",
       "      <td>6.71</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>2,15,66,24,900</td>\n",
       "      <td>796.0</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>16.0</td>\n",
       "      <td>308000</td>\n",
       "      <td>5.19</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>2,15,66,24,900</td>\n",
       "      <td>796.0</td>\n",
       "      <td>Silent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>female</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>14.0</td>\n",
       "      <td>289700</td>\n",
       "      <td>4.83</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>2,15,66,24,900</td>\n",
       "      <td>796.0</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21800</td>\n",
       "      <td>4.59</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>2,15,66,24,900</td>\n",
       "      <td>796.0</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1987</td>\n",
       "      <td>male</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>9.0</td>\n",
       "      <td>274300</td>\n",
       "      <td>3.28</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>2,15,66,24,900</td>\n",
       "      <td>796.0</td>\n",
       "      <td>Boomers</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country  year     sex          age  suicides_no  population  \\\n",
       "0  Albania  1987    male  15-24 years         21.0      312900   \n",
       "1  Albania  1987    male  35-54 years         16.0      308000   \n",
       "2  Albania  1987  female  15-24 years         14.0      289700   \n",
       "3  Albania  1987    male    75+ years          1.0       21800   \n",
       "4  Albania  1987    male  25-34 years          9.0      274300   \n",
       "\n",
       "   suicides/100k pop country-year  gdp_for_year ($)   gdp_per_capita ($)  \\\n",
       "0               6.71  Albania1987     2,15,66,24,900               796.0   \n",
       "1               5.19  Albania1987     2,15,66,24,900               796.0   \n",
       "2               4.83  Albania1987     2,15,66,24,900               796.0   \n",
       "3               4.59  Albania1987     2,15,66,24,900               796.0   \n",
       "4               3.28  Albania1987     2,15,66,24,900               796.0   \n",
       "\n",
       "        generation  \n",
       "0     Generation X  \n",
       "1           Silent  \n",
       "2     Generation X  \n",
       "3  G.I. Generation  \n",
       "4          Boomers  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean the dataframe by removing \"HDI for year\" column \n",
    "suicide_rates_df = suicide_rates_df.drop('HDI for year', axis=1)\n",
    "suicide_rates_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e644d329",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "114"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of countries\n",
    "country_count = suicide_rates_df['country'].nunique()\n",
    "country_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "34ac0b92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                object\n",
       "year                    int64\n",
       "sex                    object\n",
       "age                    object\n",
       "suicides_no           float64\n",
       "population              int64\n",
       "suicides/100k pop     float64\n",
       "country-year           object\n",
       " gdp_for_year ($)      object\n",
       "gdp_per_capita ($)    float64\n",
       "generation             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying the types of data\n",
    "suicide_rates_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d88833e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>suicides_no</th>\n",
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       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
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       "      <th>Antigua and Barbuda</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>2140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>2202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>1825.0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                     suicides_no\n",
       "country                         \n",
       "Antigua and Barbuda          1.0\n",
       "Argentina                 2140.0\n",
       "Armenia                     93.0\n",
       "Australia                 2202.0\n",
       "Austria                   1825.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying the suicide count for 1990\n",
    "suicide_rates_1990 = suicide_rates_df[suicide_rates_df['year'] == 1990]\n",
    "suicide_rates_1990_cleaned=suicide_rates_1990.dropna()\n",
    "grouped_by_year_1990 = pd.DataFrame(suicide_rates_1990.groupby('country')['suicides_no'].sum())\n",
    "grouped_by_year_1990.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "69d245c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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>3254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3318.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>1113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>9892.0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "           suicides_no\n",
       "country               \n",
       "Argentina       3254.0\n",
       "Armenia           49.0\n",
       "Australia       3318.0\n",
       "Austria         1113.0\n",
       "Brazil          9892.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying the suicide count for 2019\n",
    "suicide_rates_2019 = suicide_rates_df[suicide_rates_df['year'] == 2019]\n",
    "suicide_rates_2019_cleaned=suicide_rates_2019.dropna()\n",
    "grouped_by_year_2019 = pd.DataFrame(suicide_rates_2019_cleaned.groupby('country')['suicides_no'].sum())\n",
    "grouped_by_year_2019.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9d5db1ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>suicides_no</th>\n",
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       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>2140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>2202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>1825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>4803.0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "           suicides_no\n",
       "country               \n",
       "Argentina       2140.0\n",
       "Armenia           93.0\n",
       "Australia       2202.0\n",
       "Austria         1825.0\n",
       "Brazil          4803.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping countires that are present in both 1990 and 2019\n",
    "common_country_list = set(grouped_by_year_1990.index) & set(grouped_by_year_2019.index)\n",
    "grouped_by_year_1990 = grouped_by_year_1990[grouped_by_year_1990.index.isin(common_country_list)]\n",
    "grouped_by_year_1990.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3f29ddad",
   "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>3254.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>3318.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>1113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>9892.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           suicides_no\n",
       "country               \n",
       "Argentina       3254.0\n",
       "Armenia           49.0\n",
       "Australia       3318.0\n",
       "Austria         1113.0\n",
       "Brazil          9892.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_by_year_2019 = grouped_by_year_2019[grouped_by_year_2019.index.isin(common_country_list)]\n",
    "grouped_by_year_2019.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "99dcbad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>gdp_for_year ($)</th>\n",
       "      <th>gdp_per_capita ($)</th>\n",
       "      <th>generation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27902</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>2019</td>\n",
       "      <td>male</td>\n",
       "      <td>5-14 years</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92117</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Antigua and Barbuda2019</td>\n",
       "      <td>1687533333</td>\n",
       "      <td>17376.649680</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27903</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2019</td>\n",
       "      <td>male</td>\n",
       "      <td>5-14 years</td>\n",
       "      <td>39.0</td>\n",
       "      <td>44938712</td>\n",
       "      <td>0.086785</td>\n",
       "      <td>Argentina2019</td>\n",
       "      <td>4.52E+11</td>\n",
       "      <td>10056.637940</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27904</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>2019</td>\n",
       "      <td>male</td>\n",
       "      <td>5-14 years</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2820602</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Armenia2019</td>\n",
       "      <td>13619291361</td>\n",
       "      <td>4604.646324</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27905</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2019</td>\n",
       "      <td>male</td>\n",
       "      <td>5-14 years</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25340217</td>\n",
       "      <td>0.039463</td>\n",
       "      <td>Australia2019</td>\n",
       "      <td>1.39E+12</td>\n",
       "      <td>54875.285960</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27906</th>\n",
       "      <td>Austria</td>\n",
       "      <td>2019</td>\n",
       "      <td>male</td>\n",
       "      <td>5-14 years</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8879920</td>\n",
       "      <td>0.011261</td>\n",
       "      <td>Austria2019</td>\n",
       "      <td>4.45E+11</td>\n",
       "      <td>50114.401110</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31587</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>2019</td>\n",
       "      <td>female</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>264.0</td>\n",
       "      <td>44386203</td>\n",
       "      <td>0.594779</td>\n",
       "      <td>Ukraine2019</td>\n",
       "      <td>1.54E+11</td>\n",
       "      <td>3661.456204</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31588</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2019</td>\n",
       "      <td>female</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66836327</td>\n",
       "      <td>0.127176</td>\n",
       "      <td>United Kingdom2019</td>\n",
       "      <td>2.88E+12</td>\n",
       "      <td>43070.498360</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31589</th>\n",
       "      <td>United States of America</td>\n",
       "      <td>2019</td>\n",
       "      <td>female</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>568.0</td>\n",
       "      <td>328329953</td>\n",
       "      <td>0.172997</td>\n",
       "      <td>United States of America2019</td>\n",
       "      <td>2.14E+13</td>\n",
       "      <td>65279.529030</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31590</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>2019</td>\n",
       "      <td>female</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3428409</td>\n",
       "      <td>0.670865</td>\n",
       "      <td>Uruguay2019</td>\n",
       "      <td>61231149881</td>\n",
       "      <td>17688.015010</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31591</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2019</td>\n",
       "      <td>female</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33580350</td>\n",
       "      <td>0.014890</td>\n",
       "      <td>Uzbekistan2019</td>\n",
       "      <td>59907674027</td>\n",
       "      <td>1784.009816</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>984 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        country  year     sex         age  suicides_no  \\\n",
       "27902       Antigua and Barbuda  2019    male  5-14 years          NaN   \n",
       "27903                 Argentina  2019    male  5-14 years         39.0   \n",
       "27904                   Armenia  2019    male  5-14 years          0.0   \n",
       "27905                 Australia  2019    male  5-14 years         10.0   \n",
       "27906                   Austria  2019    male  5-14 years          1.0   \n",
       "...                         ...   ...     ...         ...          ...   \n",
       "31587                   Ukraine  2019  female   75+ years        264.0   \n",
       "31588            United Kingdom  2019  female   75+ years         85.0   \n",
       "31589  United States of America  2019  female   75+ years        568.0   \n",
       "31590                   Uruguay  2019  female   75+ years         23.0   \n",
       "31591                Uzbekistan  2019  female   75+ years          5.0   \n",
       "\n",
       "       population  suicides/100k pop                  country-year  \\\n",
       "27902       92117           0.000000       Antigua and Barbuda2019   \n",
       "27903    44938712           0.086785                 Argentina2019   \n",
       "27904     2820602           0.000000                   Armenia2019   \n",
       "27905    25340217           0.039463                 Australia2019   \n",
       "27906     8879920           0.011261                   Austria2019   \n",
       "...           ...                ...                           ...   \n",
       "31587    44386203           0.594779                   Ukraine2019   \n",
       "31588    66836327           0.127176            United Kingdom2019   \n",
       "31589   328329953           0.172997  United States of America2019   \n",
       "31590     3428409           0.670865                   Uruguay2019   \n",
       "31591    33580350           0.014890                Uzbekistan2019   \n",
       "\n",
       "       gdp_for_year ($)   gdp_per_capita ($)       generation  \n",
       "27902         1687533333        17376.649680     Generation X  \n",
       "27903           4.52E+11        10056.637940     Generation X  \n",
       "27904        13619291361         4604.646324     Generation X  \n",
       "27905           1.39E+12        54875.285960     Generation X  \n",
       "27906           4.45E+11        50114.401110     Generation X  \n",
       "...                  ...                 ...              ...  \n",
       "31587           1.54E+11         3661.456204  G.I. Generation  \n",
       "31588           2.88E+12        43070.498360  G.I. Generation  \n",
       "31589           2.14E+13        65279.529030  G.I. Generation  \n",
       "31590        61231149881        17688.015010  G.I. Generation  \n",
       "31591        59907674027         1784.009816  G.I. Generation  \n",
       "\n",
       "[984 rows x 11 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouped fianl Dataframe \n",
    "grouped_by_year_2019.groupby([\"country\"])[[\"suicides_no\"]]\n",
    "suicide_rates_2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cdad3de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">suicides/100k pop</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>var</th>\n",
       "      <th>std</th>\n",
       "      <th>sem</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>3.502879</td>\n",
       "      <td>2.970</td>\n",
       "      <td>11.778946</td>\n",
       "      <td>3.432047</td>\n",
       "      <td>0.211228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <td>0.481559</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10.018676</td>\n",
       "      <td>3.165229</td>\n",
       "      <td>0.164109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>9.324805</td>\n",
       "      <td>4.680</td>\n",
       "      <td>133.655526</td>\n",
       "      <td>11.560948</td>\n",
       "      <td>0.564117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia</th>\n",
       "      <td>2.836908</td>\n",
       "      <td>1.355</td>\n",
       "      <td>13.675197</td>\n",
       "      <td>3.697999</td>\n",
       "      <td>0.198806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>9.503095</td>\n",
       "      <td>0.000</td>\n",
       "      <td>625.830928</td>\n",
       "      <td>25.016613</td>\n",
       "      <td>1.930074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    suicides/100k pop                                        \n",
       "                                 mean median         var        std       sem\n",
       "country                                                                      \n",
       "Albania                      3.502879  2.970   11.778946   3.432047  0.211228\n",
       "Antigua and Barbuda          0.481559  0.000   10.018676   3.165229  0.164109\n",
       "Argentina                    9.324805  4.680  133.655526  11.560948  0.564117\n",
       "Armenia                      2.836908  1.355   13.675197   3.697999  0.198806\n",
       "Aruba                        9.503095  0.000  625.830928  25.016613  1.930074"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summarizing key statistics related to suicide rates\n",
    "summery_df = suicide_rates_df.groupby([\"country\"])[[\"suicides/100k pop\"]].agg([\"mean\", \"median\", \"var\", \"std\", \"sem\"])\n",
    "summery_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4826e192",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a folder for plots\n",
    "output = 'Output_Files'\n",
    "\n",
    "# Create the folder if it doesn't exist\n",
    "if not os.path.exists(output):\n",
    "    os.makedirs(output)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfc6d626",
   "metadata": {},
   "source": [
    "## Explore the correlation between suicide rates and geographical regions by comparing suicide rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "acd41524",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create an array of country names\n",
    "countries = grouped_by_year_1990.index\n",
    "\n",
    "# Create an array of indices to position the bars for 1990 and 2019\n",
    "x = np.arange(len(countries))\n",
    "\n",
    "# Width of each bar\n",
    "width = 0.35\n",
    "\n",
    "# Create subplots for side-by-side comparison\n",
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "\n",
    "# Plot for 1990\n",
    "ax.bar(x - width/2, grouped_by_year_1990['suicides_no'], width, label='1990')\n",
    "# Plot for 2019\n",
    "ax.bar(x + width/2, grouped_by_year_2019['suicides_no'], width, label='2019')\n",
    "\n",
    "# Set the x-axis ticks and labels\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(countries, rotation=90)\n",
    "ax.set_xlabel('Country', fontweight='bold')\n",
    "ax.set_ylabel('Number of Suicides', fontweight='bold')\n",
    "ax.set_title('Comparison of Suicides in 1990 and 2019 by Country',fontweight='bold')\n",
    "\n",
    "# Set the legend\n",
    "ax.legend(title='Year')\n",
    "\n",
    "# Save the file \n",
    "plt.savefig(os.path.join(output, 'suicides_comparison_1990_vs_2019.png'), dpi=300, bbox_inches='tight')\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f727b119",
   "metadata": {},
   "source": [
    "### Analysis of Number of Suicides per Country (1990 vs. 2019)\n",
    "We employed a bar graph to visually represent the number of suicides in each country for two critical years: 1990 and 2019. This analysis offers essential insights into the evolving landscape of suicide rates over the two decades.\n",
    "#### High Suicide Rates in 1990:\n",
    "- Countries such as Germany, Japan, Russia, and Ukraine exhibited notably high suicide rates in 1990. This observation reflects the challenges these nations faced in addressing mental health and suicide prevention during that period.\n",
    "#### Changes in Suicide Rates:\n",
    "- Contrasting this, it's important to highlight countries like Brazil, Mexico, South Korea, and the United Kingdom. These countries have experienced an increase in their suicide rates by the year 2019.\n",
    "#### Overall Trends:\n",
    "- One notable trend emerging from this analysis is the reduction in the overall number of suicides between 1990 and 2019. This reduction is indicative of potential progress in suicide prevention and mental health support strategies over the years.\n",
    "In both hemispheres, Latitude is a significant predictor of Max Temperature, with clear linear trends."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c50871",
   "metadata": {},
   "source": [
    "## Determine which age groups are most affected by suicide based on total suicide count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f70b1c81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_order = ['5-14 years', '15-24 years', '25-34 years', '35-54 years', '55-74 years', '75+ years']\n",
    "\n",
    "age_suicide_counts = (\n",
    "    suicide_rates_df.groupby('age')['suicides_no']\n",
    "    .sum()\n",
    "    .reindex(age_order)\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(age_suicide_counts['age'], age_suicide_counts['suicides_no'])\n",
    "\n",
    "# Set the x-axis ticks and labels\n",
    "plt.xlabel('Age Group', fontweight='bold')\n",
    "plt.ylabel('Total Suicides Count', fontweight='bold')\n",
    "plt.title('Total Suicides Count by Age Group', fontweight='bold')\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# Save the file \n",
    "plt.savefig(os.path.join(output, 'suicides_by_age_group_per_total.png'), dpi=300, bbox_inches='tight')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "444dca6f",
   "metadata": {},
   "source": [
    "### Analysis of Total Suicides by Age Group:\n",
    "In the first graph, which represents the total number of suicides in each age group, we observe the following:\n",
    "- The age group with the highest number of suicides is between 35 and 54 years. This age category shows the highest count of suicides, indicating a critical age range for suicide prevention efforts.\n",
    "- Following closely is the age group between 55 and 74 years, with the second-highest number of suicides. This highlights the significance of addressing mental health concerns in this age range."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c119876",
   "metadata": {},
   "source": [
    "## Determine which age groups are most affected by suicide based on suicides per 100K of population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2551abc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_order = ['5-14 years', '15-24 years', '25-34 years', '35-54 years', '55-74 years', '75+ years']\n",
    "\n",
    "age_suicide_counts = (\n",
    "    suicide_rates_df.groupby('age')['suicides/100k pop']\n",
    "    .sum()\n",
    "    .reindex(age_order)\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(age_suicide_counts['age'], age_suicide_counts['suicides/100k pop'])\n",
    "\n",
    "# Set the x-axis ticks and labels\n",
    "plt.xlabel('Age Group', fontweight='bold')\n",
    "plt.ylabel('Suicides per 100k Population', fontweight='bold')\n",
    "plt.title('Total Suicides Count by Age Group', fontweight='bold')\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# Save the file \n",
    "plt.savefig(os.path.join(output, 'suicides_by_age_group_per_100K.png'), dpi=300, bbox_inches='tight')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4076500d",
   "metadata": {},
   "source": [
    "### Analysis of Suicides per 100,000 Population by Age Group:\n",
    "In the second graph, which represents suicides per 100,000 population in each age group, the observations are different:\n",
    "- The age group with the highest rate of suicides per 100,000 population is the \"75+ years\" category. This indicates a higher risk of suicide for individuals in this age group when considering population size.\n",
    "- The second-highest rate of suicides per 100,000 population is in the \"55-74 years\" age bracket. This finding emphasizes that, when adjusted for population size, middle-aged and early elderly individuals face a relatively higher risk of suicide."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d20f62",
   "metadata": {},
   "source": [
    "## Analyze the relationship between suicide rates and gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "07c70518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group by 'sex' and calculate the sum of 'suicides_no' for each gender\n",
    "gender_suicide_counts = suicide_rates_df.groupby('sex')['suicides_no'].sum().reset_index()\n",
    "\n",
    "# Create a bar plot to compare male and female suicide counts\n",
    "plt.figure(figsize=(8, 6))\n",
    "labels = gender_suicide_counts['sex']\n",
    "sizes = gender_suicide_counts['suicides_no']\n",
    "\n",
    "#plot chart\n",
    "plt.pie(sizes, labels=labels, autopct=\"%1.1f%%\", shadow=True, startangle=140)\n",
    "\n",
    "# labels and a title & save\n",
    "plt.xlabel('Gender', fontweight='bold')\n",
    "plt.title('Total Suicides Count by Gender', fontweight='bold')\n",
    "\n",
    "# Save the file \n",
    "plt.savefig(os.path.join(output, 'suicides_by_gender_total.png'), dpi=300, bbox_inches='tight')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbb8c141",
   "metadata": {},
   "source": [
    "### Analysis of Total Suicides by Gender:\n",
    "In this analysis, we employed a pie chart to provide a clear visual representation of the distribution of total suicides by gender. This examination allows for a straightforward comparison of suicide counts between males and females.\n",
    "#### Men significantly outnumber women in suicide cases:\n",
    "- The pie chart reveals a striking gender imbalance in suicide rates. Males account for a substantial majority, representing approximately 76.5% of the total suicides.\n",
    "- Conversely, females constitute a smaller portion of the total suicides, accounting for approximately 23.5%. This percentage is notably lower compared to males.\n",
    "\n",
    "A key observation is that males have approximately three times the number of suicides compared to females. This significant gender disparity underscores the need for gender-specific approaches in suicide prevention efforts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07e10ca7",
   "metadata": {},
   "source": [
    "## Analyze the relationship between suicide rates and gender by comparing suicide rates in 1990 and 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "89960970",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter the data for the year 1990 and group by \"sex\" to get suicide counts\n",
    "suicide_data_1990 = suicide_rates_df[suicide_rates_df['year'] == 1990]\n",
    "gender_suicide_counts_1990 = suicide_data_1990.groupby('sex')['suicides/100k pop'].sum().reset_index()\n",
    "\n",
    "# Filter the data for the year 2019 and group by \"sex\" to get suicide counts\n",
    "suicide_data_2019 = suicide_rates_df[suicide_rates_df['year'] == 2019]\n",
    "gender_suicide_counts_2019 = suicide_data_2019.groupby('sex')['suicides/100k pop'].sum().reset_index()\n",
    "\n",
    "# Create an array of country names\n",
    "sex = pd.DataFrame(suicide_data_1990.groupby('sex'))\n",
    "sex = sex[0].value_counts().index\n",
    "\n",
    "\n",
    "# Create an array of indices to position the bars for 1990 and 2019\n",
    "x = np.arange(len(sex))\n",
    "\n",
    "# Width of each bar\n",
    "width = 0.35\n",
    "\n",
    "# Create subplots for side-by-side comparison\n",
    "fig, ax = plt.subplots(figsize=(12, 5))\n",
    "\n",
    "# Plot for 1990\n",
    "ax.bar(x - width/2, gender_suicide_counts_1990['suicides/100k pop'], width, label='1990')\n",
    "# Plot for 2019\n",
    "ax.bar(x + width/2, gender_suicide_counts_2019['suicides/100k pop'], width, label='2019')\n",
    "\n",
    "# Set the x-axis ticks and labels\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(sex)\n",
    "ax.set_xlabel('Gender',fontweight='bold')\n",
    "ax.set_ylabel('Suicide Counts /100k pop ',fontweight='bold')\n",
    "ax.set_title('Suicide Counts by Gender (1990 vs. 2019)',fontweight='bold')\n",
    "ax.set_ylim([0, 8000])\n",
    "\n",
    "# Set the legend\n",
    "ax.legend(title='Year')\n",
    "\n",
    "# Save the file \n",
    "plt.savefig(os.path.join(output, 'suicides_by_gender_per_100K_1990_vs_2019.png'), dpi=300, bbox_inches='tight')\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "587273dd",
   "metadata": {},
   "source": [
    "### Analysis of Total Suicides per 100k Population by Gender (1990 vs. 2019):\n",
    "In this analysis, we employed a bar graph to examine the total suicides per 100k population for males and females in both 1990 and 2019. This visual comparison allows us to identify gender-specific trends in suicide rates over the two decades.\n",
    "#### Suicide Rates in 1990 (per 100k population):\n",
    "- For males in 1990, the suicide rate was a little over 2000, indicating a significant number of suicides in this gender group.\n",
    "- For females in 1990, the suicide rate was approximately 1600, reflecting a substantial but relatively lower number of suicides compared to males.\n",
    "#### Suicide Rates in 2019 (per 100k population):\n",
    "In 2019, there has been a notable increase in suicide rates for both genders.\n",
    "- For males in 2019, the suicide rate has risen to almost 8000 per 100k population, signifying a considerable surge in suicides among males over the years.\n",
    "- For females in 2019, the suicide rate has also increased, exceeding 3000 per 100k population, which is significantly higher compared to 1990."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0007962",
   "metadata": {},
   "source": [
    "## Examine the correlation between GDP per capita and suicide rates per 100k Population."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f67e43be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to create Linear Regression plots\n",
    "def plot_lin_reg(x_values, y_values,text_coordinates):\n",
    "    (slope, intercept, r_value, p_value, std_err) = linregress(x_values, y_values)\n",
    "    regress_values = x_values * slope + intercept\n",
    "    line_eq = \"y = \" + str(round(slope,2)) + \"x + \" + str(round(intercept,2))\n",
    "    plt.scatter(x_values,y_values)\n",
    "    plt.plot(x_values,regress_values,\"r-\")\n",
    "    plt.annotate(line_eq, text_coordinates, fontsize=15, color=\"red\")\n",
    "    print(f\"The r-value is: {r_value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "23ee1f35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is: 0.0025963706087137393\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the values\n",
    "x_values = suicide_rates_df['gdp_per_capita ($)']\n",
    "y_values = suicide_rates_df['suicides/100k pop']\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Set the labels\n",
    "plt.xlabel('GDP per Capita', fontweight='bold')\n",
    "plt.ylabel('Suicides per 100k Population', fontweight='bold')  \n",
    "plt.title('Scatter Plot of GDP per Capita vs. Suicides per 100k Population', fontweight='bold')  \n",
    "plt.grid(True)\n",
    "plot_lin_reg(x_values, y_values,(80000,450))\n",
    "\n",
    "# Save the file \n",
    "plt.savefig(os.path.join(output, 'suicides_per_100K_vs_GDP.png'), dpi=300, bbox_inches='tight')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "539fcad5",
   "metadata": {},
   "source": [
    "### Analysis of GDP per Capita vs. Suicides per 100k Population:\n",
    "This scatter plot examines the relationship between GDP per capita and suicide rates across about 40 countries. The goal is to identify trends and patterns in this relationship.\n",
    "- Rising Suicide Rates with GDP Increase: Suicide rates tend to increase as GDP per capita rises from 20,000 to 45,000, indicating potential socioeconomic pressures.\n",
    "- Stable Rates Beyond 45,000: Beyond a GDP per capita of approximately 45,000 to 50,000, suicide rates stabilize around 50 per 100k population.\n",
    "- Outliers at High Income Levels: Some countries with high GDP per capita exhibit unexpectedly high suicide rates, emphasizing the role of non-economic factors.\n",
    "\n",
    "While there's a general trend of increasing suicide rates with rising GDP per capita up to a point, this relationship is complex. Mental health support systems, cultural attitudes, and other factors also influence suicide rates."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8323a2a0",
   "metadata": {},
   "source": [
    "## Exporting and cleaning a Mental Health Expenditure DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1cb33bc5",
   "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>% of Gov Exp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Peru</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            country  % of Gov Exp\n",
       "0          Zimbabwe          0.01\n",
       "1  Papua New Guinea          0.10\n",
       "2        Mozambique          0.16\n",
       "3          Cameroon          0.26\n",
       "4              Peru          0.27"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mental_health_expenditure = Path(\"Resources/health_expenditure_2011.csv\")\n",
    "\n",
    "mental_health_expenditure_df = pd.read_csv(mental_health_expenditure)\n",
    "\n",
    "selected_columns = mental_health_expenditure_df[[\"Location\", \"Value\"]]\n",
    "\n",
    "selected_columns.columns = [\"country\", \"% of Gov Exp\"]\n",
    "\n",
    "mental_health_expenditure_df = selected_columns\n",
    "\n",
    "mental_health_expenditure_df.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "871290c9",
   "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>% of Gov Exp</th>\n",
       "      <th>suicides/100k pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>0.01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>0.10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>0.16</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cameroon</td>\n",
       "      <td>0.26</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Peru</td>\n",
       "      <td>0.27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            country  % of Gov Exp  suicides/100k pop\n",
       "0          Zimbabwe          0.01                NaN\n",
       "1  Papua New Guinea          0.10                NaN\n",
       "2        Mozambique          0.16                NaN\n",
       "3          Cameroon          0.26                NaN\n",
       "4              Peru          0.27                NaN"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The original DataFrame for the year 2011\n",
    "suicides_2011_df = suicide_rates_df[suicide_rates_df['year'] == 2011]\n",
    "\n",
    "# Grouprf the filtered data by 'country' and summed the 'suicides_no' values\n",
    "suicides_by_country_2011 = suicides_2011_df.groupby('country')['suicides/100k pop'].sum().reset_index()\n",
    "\n",
    "# Merged the DataFrame with the summed suicides data based on 'country'\n",
    "merged_df = mental_health_expenditure_df.merge(suicides_by_country_2011, on='country', how='left')\n",
    "\n",
    "# Renamed the newly added column\n",
    "merged_df.rename(columns={'suicides_no': 'suicides/100k pop'}, inplace=True)\n",
    "\n",
    "merged_df.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d0845d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 3)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing rows with NaN values from the DataFrame\n",
    "merged_df_cleaned = merged_df.dropna()\n",
    "\n",
    "# Cleaned DataFrame\n",
    "merged_df_cleaned.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e739fae7",
   "metadata": {},
   "source": [
    "## Visualize the relationship between the percentage of government expenditure and suicide rates per 100k population for the year 2011."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5f269d7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r-value is: 0.24422519933684808\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the values\n",
    "x_values = merged_df_cleaned['% of Gov Exp']\n",
    "y_values = merged_df_cleaned['suicides/100k pop']\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Set the labels\n",
    "plt.xlabel('Percentage of Government Expenditure', fontweight='bold')\n",
    "plt.ylabel('suicides/100k pop', fontweight='bold')\n",
    "plt.title('Scatter Plot of Government Expenditure vs. suicides/100k pop (2011)', fontweight='bold')\n",
    "plot_lin_reg(x_values, y_values,(8,320))\n",
    "\n",
    "# Save the file \n",
    "plt.savefig(os.path.join(output, 'suicides_per_100K_vs_Government_Expenditure.png'), dpi=300, bbox_inches='tight')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
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   "cell_type": "markdown",
   "id": "2de7ef84",
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   "source": [
    "### Analysis of the relationship between the Percentage of Government Expenditure and Suicide Rates per 100k Population for the year 2011:\n",
    "This scatter plot examines the relationship between percentage of government expenditure and suicide rates across about 40 countries. The goal is to identify trends and patterns in this relationship.\n",
    "- Rising Suicide Rates with increase in government expenditure: The positive slope coefficient indicates a positive relationship between government expenditure and suicides per 100k population.\n",
    "- In other words, as government expenditure increases, the estimated suicide rate tends to increase. This indicates that there are other factors, such as social, economic, or demographic factors, that influence both government expenditure and suicide rates."
   ]
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   "id": "835b02c5",
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   "outputs": [],
   "source": []
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