suicide_rates_overview_in_brazil.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Suicide Rate Overview in Brazil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every year, more than 12k people finish off your own life's in Brazil. In the world scenario this number is more than 1 million. This fact make the suicide become a public health problem and for that,  many iniciatives from goverment were created. One of there is the Yellow September,  created by Brazilian Psychiatric Association (ABP) in partnership with the Federal Council of Medicine (CFM)\n",
    "\n",
    "In this study I will show some analysis about the data and make some insights about the problem. Feel comfortable to copy the notebook and make your own analysis.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description of Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is hosted in [that page](https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016), is a compile of various datasets since 1985 at 2016, and bring info about many countries. I start the analysis making the import of some libraries that I use.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Datasets/suicide_rates_overview/master.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables Dictionary:\n",
    "\n",
    "### This dataset has 12 columns with the content below.\n",
    "\n",
    "- **country:** country where the data has collected\n",
    "  - 101 países\n",
    "- **year:** year when the data has registered\n",
    "  - 1987 a 2016\n",
    "- **sex:** sex considered on the registries\n",
    "  - male\n",
    "  - female\n",
    "- **age:** age range considered on the registries\n",
    "  - 5-14 years old\n",
    "  - 15-24 years old\n",
    "  - 25-34 years old\n",
    "  - 35-54 years old\n",
    "  - 55-74 years old\n",
    "  - 75+ years old\n",
    "- **suicides_no:** total occurancy of suicides\n",
    "- **opulation:** group population\n",
    "- **suicides/100k pop:** number of suicides per 100k inhabitants \n",
    "- **country_year:** id count country + year\n",
    "- **HDI for year:** Human Development Index per year\n",
    "- **gdp_for_year:** Gross Domestic Product per year\n",
    "- **gdp_per_capita:** Gross Domestic Product per capita"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data have a previous treatment made from the team has provided it. Dataset has 12 columns and 27.820 rows. Below we encounter a simple analysis to exploration the data and make our first insights about the subject."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframe Shape:  (27820, 12)\n"
     ]
    },
    {
     "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>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</td>\n",
       "      <td>312900</td>\n",
       "      <td>6.71</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</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</td>\n",
       "      <td>308000</td>\n",
       "      <td>5.19</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</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</td>\n",
       "      <td>289700</td>\n",
       "      <td>4.83</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</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</td>\n",
       "      <td>21800</td>\n",
       "      <td>4.59</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</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</td>\n",
       "      <td>274300</td>\n",
       "      <td>3.28</td>\n",
       "      <td>Albania1987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2,156,624,900</td>\n",
       "      <td>796</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      312900   \n",
       "1  Albania  1987    male  35-54 years           16      308000   \n",
       "2  Albania  1987  female  15-24 years           14      289700   \n",
       "3  Albania  1987    male    75+ years            1       21800   \n",
       "4  Albania  1987    male  25-34 years            9      274300   \n",
       "\n",
       "   suicides/100k pop country-year  HDI for year  gdp_for_year ($)   \\\n",
       "0               6.71  Albania1987           NaN      2,156,624,900   \n",
       "1               5.19  Albania1987           NaN      2,156,624,900   \n",
       "2               4.83  Albania1987           NaN      2,156,624,900   \n",
       "3               4.59  Albania1987           NaN      2,156,624,900   \n",
       "4               3.28  Albania1987           NaN      2,156,624,900   \n",
       "\n",
       "   gdp_per_capita ($)       generation  \n",
       "0                 796     Generation X  \n",
       "1                 796           Silent  \n",
       "2                 796     Generation X  \n",
       "3                 796  G.I. Generation  \n",
       "4                 796          Boomers  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Dataframe Shape: \", df.shape)\n",
    "\n",
    "# First 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first thing to do is group cases by a specific country, in that case Brazil, that is make more easy to create our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframe Shape:  (372, 12)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>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>4172</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>75+ years</td>\n",
       "      <td>129</td>\n",
       "      <td>840300</td>\n",
       "      <td>15.35</td>\n",
       "      <td>Brazil1985</td>\n",
       "      <td>0.576</td>\n",
       "      <td>222,942,790,435</td>\n",
       "      <td>1898</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4173</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>55-74 years</td>\n",
       "      <td>535</td>\n",
       "      <td>5364900</td>\n",
       "      <td>9.97</td>\n",
       "      <td>Brazil1985</td>\n",
       "      <td>0.576</td>\n",
       "      <td>222,942,790,435</td>\n",
       "      <td>1898</td>\n",
       "      <td>G.I. Generation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4174</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>35-54 years</td>\n",
       "      <td>1011</td>\n",
       "      <td>11986300</td>\n",
       "      <td>8.43</td>\n",
       "      <td>Brazil1985</td>\n",
       "      <td>0.576</td>\n",
       "      <td>222,942,790,435</td>\n",
       "      <td>1898</td>\n",
       "      <td>Silent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4175</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>25-34 years</td>\n",
       "      <td>791</td>\n",
       "      <td>10762900</td>\n",
       "      <td>7.35</td>\n",
       "      <td>Brazil1985</td>\n",
       "      <td>0.576</td>\n",
       "      <td>222,942,790,435</td>\n",
       "      <td>1898</td>\n",
       "      <td>Boomers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4176</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1985</td>\n",
       "      <td>male</td>\n",
       "      <td>15-24 years</td>\n",
       "      <td>661</td>\n",
       "      <td>13834000</td>\n",
       "      <td>4.78</td>\n",
       "      <td>Brazil1985</td>\n",
       "      <td>0.576</td>\n",
       "      <td>222,942,790,435</td>\n",
       "      <td>1898</td>\n",
       "      <td>Generation X</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country  year   sex          age  suicides_no  population  \\\n",
       "4172  Brazil  1985  male    75+ years          129      840300   \n",
       "4173  Brazil  1985  male  55-74 years          535     5364900   \n",
       "4174  Brazil  1985  male  35-54 years         1011    11986300   \n",
       "4175  Brazil  1985  male  25-34 years          791    10762900   \n",
       "4176  Brazil  1985  male  15-24 years          661    13834000   \n",
       "\n",
       "      suicides/100k pop country-year  HDI for year  gdp_for_year ($)   \\\n",
       "4172              15.35   Brazil1985         0.576    222,942,790,435   \n",
       "4173               9.97   Brazil1985         0.576    222,942,790,435   \n",
       "4174               8.43   Brazil1985         0.576    222,942,790,435   \n",
       "4175               7.35   Brazil1985         0.576    222,942,790,435   \n",
       "4176               4.78   Brazil1985         0.576    222,942,790,435   \n",
       "\n",
       "      gdp_per_capita ($)       generation  \n",
       "4172                1898  G.I. Generation  \n",
       "4173                1898  G.I. Generation  \n",
       "4174                1898           Silent  \n",
       "4175                1898          Boomers  \n",
       "4176                1898     Generation X  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract the brazilian data\n",
    "df_brazil = df[df.country == \"Brazil\"].copy()\n",
    "\n",
    "# The new dataframe\n",
    "print(\"Dataframe Shape: \", df_brazil.shape)\n",
    "\n",
    "# five rows of the new dataframe\n",
    "df_brazil.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we see that HDI variable has a lot of null values, make that variable useless for our analysis. If we decided use that information that is necessary to search in others sources of data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--> World:\n"
     ]
    },
    {
     "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          19456\n",
       " gdp_for_year ($)         0\n",
       "gdp_per_capita ($)        0\n",
       "generation                0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --.--.--.--.--.--.--.--.--.--. \n",
      "\n",
      "--> Brazil:\n"
     ]
    },
    {
     "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          252\n",
       " gdp_for_year ($)       0\n",
       "gdp_per_capita ($)      0\n",
       "generation              0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"--> World:\")\n",
    "display(df.isnull().sum())\n",
    "\n",
    "print(\"\\n\", \"--.\" * 10, \"\\n\")\n",
    "\n",
    "# velores ausentes nos dados do Brasil\n",
    "print(\"--> Brazil:\")\n",
    "display(df_brazil.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The suicide rate tendencies in Brazil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initially we compare the rate of suicide in 100k inhabitants at world and Brazil. This strategy is a better approach because the population grown a lot since 1985 to 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the mean of world and brazil\n",
    "years = df_brazil.year.unique() \n",
    "suicides_brazil_mean = df_brazil.groupby('year')['suicides/100k pop'].mean()\n",
    "suicides_world_mean = df.groupby('year')['suicides/100k pop'].mean()\n",
    "\n",
    "# Brazil do not have the year 2016, so we eliminate it  from world dataframe.\n",
    "suicides_world_mean.drop(2016, inplace=True)\n",
    "\n",
    "# plot comparisons from Brazil and World\n",
    "ax = sns.lineplot(x=years, y=suicides_brazil_mean, label='Brasil')\n",
    "ax = sns.lineplot(x=years, y=suicides_world_mean, label='Mundo')\n",
    "plt.legend(title=\"Taxa de suicídio\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One thing is easily observation in the graphs above. Despite the suicide rate in Brazil is minor then a World mean, the rate is grown.\n",
    "\n",
    "The orange line peaked around 1995, but reversed the slope and has been falling year after year. The blue line (Brazil) has a positive slope practically throughout the analyzed period."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age group with the highest rate of suicide\n",
    "Below, I created a pivot table to analyze the 6 age groups according to the year and the number of suicides per 100 thousand inhabitants.\n",
    "\n",
    "My main objective here is to identify the age group that has the highest representation among those who take their own lives, and to identify if there has been any change in the profile over 30 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>age</th>\n",
       "      <th>5-14 years</th>\n",
       "      <th>15-24 years</th>\n",
       "      <th>25-34 years</th>\n",
       "      <th>35-54 years</th>\n",
       "      <th>55-74 years</th>\n",
       "      <th>75+ years</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></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>1985</th>\n",
       "      <td>38.0</td>\n",
       "      <td>470.5</td>\n",
       "      <td>530.0</td>\n",
       "      <td>653.5</td>\n",
       "      <td>341.5</td>\n",
       "      <td>80.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>31.0</td>\n",
       "      <td>508.0</td>\n",
       "      <td>505.5</td>\n",
       "      <td>651.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>33.5</td>\n",
       "      <td>502.0</td>\n",
       "      <td>565.5</td>\n",
       "      <td>724.5</td>\n",
       "      <td>422.0</td>\n",
       "      <td>88.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>30.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>557.5</td>\n",
       "      <td>724.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>93.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>31.5</td>\n",
       "      <td>491.5</td>\n",
       "      <td>569.0</td>\n",
       "      <td>692.5</td>\n",
       "      <td>354.5</td>\n",
       "      <td>92.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "age   5-14 years  15-24 years  25-34 years  35-54 years  55-74 years  \\\n",
       "year                                                                   \n",
       "1985        38.0        470.5        530.0        653.5        341.5   \n",
       "1986        31.0        508.0        505.5        651.0        372.0   \n",
       "1987        33.5        502.0        565.5        724.5        422.0   \n",
       "1988        30.0        472.0        557.5        724.0        356.0   \n",
       "1989        31.5        491.5        569.0        692.5        354.5   \n",
       "\n",
       "age   75+ years  \n",
       "year             \n",
       "1985       80.5  \n",
       "1986       68.0  \n",
       "1987       88.5  \n",
       "1988       93.5  \n",
       "1989       92.5  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a pivot table\n",
    "table = pd.pivot_table(df_brazil, values='suicides_no', index=['year'], columns=['age'])\n",
    "\n",
    "# sort the tables to leave in ascending order\n",
    "column_order = ['5-14 years', '15-24 years', '25-34 years', '35-54 years', '55-74 years', '75+ years']\n",
    "table = table.reindex(column_order, axis=1)\n",
    "\n",
    "# see the first 5 pivot table entries\n",
    "table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the age group pivot table\n",
    "table.plot.bar(stacked=True, figsize=(16,8))\n",
    "plt.legend(title=\"Age\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even visually, it is easy to identify that the group of people who most commit suicide is between 35-54 years old. Second, there are people between 25-34 years old.\n",
    "\n",
    "Together, these two groups account for almost 60% of the database records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age\n",
       "5-14 years     0.012094\n",
       "15-24 years    0.159993\n",
       "25-34 years    0.211681\n",
       "35-54 years    0.371585\n",
       "55-74 years    0.194571\n",
       "75+ years      0.050076\n",
       "Name: 2015, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# suicide statistics by age group\n",
    "table[['25-34 years']].loc[2015]\n",
    "table.loc[2015] / table.loc[2015].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another point worth mentioning is the increase in cases among people over 55. In 1985, older people represented a small slice of the total number.\n",
    "\n",
    "However, in 2015 it is clear that even for people over 75 years old there was a significant increase in the number of suicides.\n",
    "\n",
    "In order to infer the cause of this change in pattern, it is necessary to evaluate more deeply issues that go beyond numbers (for example, qualitative factors, the country's economic moment and the dominant culture of each era).\n",
    "\n",
    "## Suicide rate between men and women\n",
    "Another analysis of great importance is to see the percentage of suicides between men and women.\n",
    "\n",
    "Analyzing the entire period, the dataset used showed that approximately 78% of cases were committed by men and 22% of them by women. We chose to take the average of 30 years, as there was no significant change in this behavior during the period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extracting values between men and women\n",
    "men_women = df_brazil.groupby('sex').suicides_no.sum()/df_brazil.groupby('sex').suicides_no.sum().sum()\n",
    "\n",
    "# plot the pie chart\n",
    "plt.pie(men_women, labels = ['women', 'men'], autopct = '% 1.1f %%', shadow = True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlations between GDP, HDI and number of suicides\n",
    "By creating a correlation matrix and plotting a heatmap, it appears that the increase in GDP per capita did not decrease the number of suicides per 100 thousand inhabitants. In fact, it has remained stable, contrary to the common sense of most people.\n",
    "\n",
    "Regarding the HDI, as previously mentioned, there are many values missing in the cells, which can give an incorrect or biased interpretation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotar heatmap\n",
    "corr = df_brazil.corr()\n",
    "sns.heatmap(corr, cmap='RdBu', fmt='.2f', square=True, linecolor='white', annot=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The proposal of this study is show how the Data Science has the possibility of use in real cases and create insights to solve real problems."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}