suicidio.ipynb

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      "name": "suicidio.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/OseiasBeu/Data-Science/blob/master/suicidio.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zLNoUaB_R4-f",
        "colab_type": "text"
      },
      "source": [
        "# Análise da taxa de suicídio\n",
        "\n",
        "Todos os anos, mais de 12 mil pessoas tiram suas próprias vidas no Brasil. Em um cenário mundial, esse número ultrapassa 1 milhão de pessoas, levando o suicídio a ser considerado um problema de saúde pública.\n",
        "\n",
        "Para você ter noção da dimensão desses números, saiba que [o suicídio tem uma taxa maior do que vítimas de AIDS e da maioria dos tipos de câncer](https://pt.wikipedia.org/wiki/Setembro_Amarelo). Segundo a Organização Mundial da Saúde (OMS), o Brasil ocupa o oitavo lugar no número de suicídios no mundo: São 32 brasileiros por dia.\n",
        "\n",
        "\n",
        "O intuido desse pequeno artigo é ser uma pequena contribuição que visa ajudar a entender a extensão desse problema no Brasil, além de incentivar o Setembro Amarelo dentro da comunidade Python e Data Science.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E1si-OwNZuNI",
        "colab_type": "text"
      },
      "source": [
        "## Descrição dos dados sobre suicídio\n",
        "\n",
        "O melhor conjunto de dados sobre suicidio [está hospedado nesta página](https://www.kaggle.com/russellyates88/suicide-rates-overview-1985-to-2016), e traz informações sobre vários países. O *dataset* contempla o período entre os anos de 1985 e 2016.\n",
        "\n",
        "Esse *dataset*, na verdade, é um compilado de outros 4 *datasets*. A pessoa que disponibilizou o mesmo fez um grande trabalho de limpeza e padronização. Caso você deseje tenha interesse em conhecer as fontes que deram origem a este único arquivo, são elas:\n",
        "\n",
        "* [United Nations Development Program. (2018). Human development index - HDI.](http://hdr.undp.org/en/indicators/137506)\n",
        "* [World Bank. (2018). World development indicators: GDP (current US$) by country:1985 to 2016.](http://databank.worldbank.org/data/source/world-development-indicators#)\n",
        "* [Suicide in the Twenty-First Century (Szamil, 2017).](https://www.kaggle.com/szamil/suicide-in-the-twenty-first-century/notebook)\n",
        "* [World Health Organization. (2018). Suicide prevention.](http://www.who.int/mental_health/suicide-prevention/en/)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JX-dbWTb-E5f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# importar pacotes necessários\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07Wbf53k-PN_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# importar os dados sobre suicídio para um DataFrame\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/OseiasBeu/Data-Science/master/DataSets/suicide_rates.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UVU0wYVcFFEP",
        "colab_type": "text"
      },
      "source": [
        "### Dicionário de Variáveis\n",
        "\n",
        "Esse arquivo *csv* possui 12 colunas. São elas:\n",
        "\n",
        "* **country:** país onde os dados foram registrados\n",
        "  * 101 países\n",
        "* **year:** ano em que os dados foram registrados\n",
        "  * 1987 a 2016\n",
        "* **sex:** sexo considerado no registro\n",
        "  * *male* - masculino\n",
        "  * *female* - feminino\n",
        "* **age**: faixa etária considerada\n",
        "  * 5-14 anos\n",
        "  * 15-24 anos\n",
        "  * 25-34 anos\n",
        "  * 35-54 anos\n",
        "  * 55-74 anos\n",
        "  * 75+ anos\n",
        "* **suicides_no:** número de suicídios\n",
        "* **population:** população para o grupo\n",
        "* **suicides/100k pop:** número de suicídios por 100 mil habitantes\n",
        "* **country_year:** identificador contendo `country` + `year`\n",
        "* **HDI for year:** Índice de Desenvolvimento Humano (IDH) para o ano\n",
        "* **gdp_for_year:** Produto Interno Bruto (PIB) para o ano\n",
        "* **gdp_per_capita:** Produto Interno Bruto (PIB) per capita"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jj1Aiqxlc3uR",
        "colab_type": "text"
      },
      "source": [
        "## Análise Exploratória dos Dados\n",
        "\n",
        "Como mencionei acima, este arquivo recebeu um tratamento anterior que facilitará muito a nossa análise.\n",
        "\n",
        "O conjunto de dados contém 27.820 linhas e 12 colunas. Como você pode ver abaixo, os dados podem ser agrupados por vários critérios como país, ano, sexo e idade."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jytGO2md-dNH",
        "colab_type": "code",
        "outputId": "e2c8ad0a-75ac-444b-86ca-db879fb4d82c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "# ver o formato do DataFrame\n",
        "print(\"Formato do DataFrame: \", df.shape)\n",
        "\n",
        "# ver as 5 primeiras entradas dos dataset\n",
        "df.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Formato do DataFrame:  (27820, 12)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<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",
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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</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  ... gdp_per_capita ($)       generation\n",
              "0  Albania  1987  ...                796     Generation X\n",
              "1  Albania  1987  ...                796           Silent\n",
              "2  Albania  1987  ...                796     Generation X\n",
              "3  Albania  1987  ...                796  G.I. Generation\n",
              "4  Albania  1987  ...                796          Boomers\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z_JKgWqKeYm5",
        "colab_type": "text"
      },
      "source": [
        "A primeira coisa que eu quero fazer é criar um *DataFrame* contendo apenas as informações relacionadas ao Brasil e um outro contendo informações relacionadas a Russia.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4oisAgzrevf2",
        "colab_type": "code",
        "outputId": "e1a72304-9b37-493b-ab48-68e70825d245",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "# extrair apenas os dados relativos ao Brasil\n",
        "df_brasil = df[df.country == \"Brazil\"].copy()\n",
        "df_russian = df[df.country == \"Russian Federation\"].copy()\n",
        "\n",
        "\n",
        "# ver o formato do novo DataFrame\n",
        "print(\"Formato do DataFrame brasil: \", df_brasil.shape)\n",
        "print(\"Formato do DataFrame russian: \", df_russian.shape)\n",
        "\n",
        "# exibir as 5 primeiras entradas do novo DataFrame\n",
        "df_brasil.head()"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Formato do DataFrame brasil:  (372, 12)\n",
            "Formato do DataFrame russian:  (324, 12)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "    .dataframe thead th {\n",
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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>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  ... gdp_per_capita ($)       generation\n",
              "4172  Brazil  1985  ...               1898  G.I. Generation\n",
              "4173  Brazil  1985  ...               1898  G.I. Generation\n",
              "4174  Brazil  1985  ...               1898           Silent\n",
              "4175  Brazil  1985  ...               1898          Boomers\n",
              "4176  Brazil  1985  ...               1898     Generation X\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HNQ_DlMSt1sS",
        "colab_type": "text"
      },
      "source": [
        "Agrupando por pais e fazendo a contagem de suicídio temos uma pequena lista com os paises com maior taxa de mortes nos últimos anos e vemos que o Brasil está entres os 10 paises com maior número de mortes do mundo:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GQUj8m-Tn64O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "764e0247-df23-426b-f99a-47d908436a4d"
      },
      "source": [
        "# df.country[df['suicides_no'].sum()].head(19)\n",
        "df.groupby('country').suicides_no.sum().sort_values(ascending=False).head(10)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "country\n",
              "Russian Federation    1209742\n",
              "United States         1034013\n",
              "Japan                  806902\n",
              "France                 329127\n",
              "Ukraine                319950\n",
              "Germany                291262\n",
              "Republic of Korea      261730\n",
              "Brazil                 226613\n",
              "Poland                 139098\n",
              "United Kingdom         136805\n",
              "Name: suicides_no, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KqHJi4jBHDqt",
        "colab_type": "text"
      },
      "source": [
        "Para ter uma noção da integridade dos dados, vou verificar isoladamente os *DataFrames* da Russia por ter o maior número de mortes no mundo, Brasil e o Mundial.É importante que eles sejam similares e estatisticamente representativas.\n",
        "\n",
        "Como você vê abaixo, a porcentagem de valores ausentes referentes ao IDH torna inviável o uso dessa variável. Caso se desejasse realmente usar, seria necessária uma etapa de coleta de dados a partir de outras fontes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0HS6gfPHXp1",
        "colab_type": "code",
        "outputId": "eb3522b9-b1b4-4f45-9492-b45e153e18d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        }
      },
      "source": [
        "# valores ausentes nos dados mundiais\n",
        "print(\"--> Mundial:\")\n",
        "display(df.isnull().sum())\n",
        "\n",
        "print(\"\\n\", \"--.\" * 10, \"\\n\")\n",
        "\n",
        "# velores ausentes nos dados do Brasil\n",
        "print(\"--> Brasil:\")\n",
        "display(df_brasil.isnull().sum())\n",
        "\n",
        "print(\"\\n\", \"--.\" * 10, \"\\n\")\n",
        "\n",
        "# velores ausentes nos dados da Russia\n",
        "print(\"--> Russia:\")\n",
        "display(df_russian.isnull().sum())\n"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--> Mundial:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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 ($)        0\n",
              "generation                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " --.--.--.--.--.--.--.--.--.--. \n",
            "\n",
            "--> Brasil:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "country                 0\n",
              "year                    0\n",
              "sex                     0\n",
              "age                     0\n",
              "suicides_no             0\n",
              "population              0\n",
              "suicides/100k pop       0\n",
              "country-year            0\n",
              "HDI for year          252\n",
              " gdp_for_year ($)       0\n",
              "gdp_per_capita ($)      0\n",
              "generation              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            " --.--.--.--.--.--.--.--.--.--. \n",
            "\n",
            "--> Russia:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "country                 0\n",
              "year                    0\n",
              "sex                     0\n",
              "age                     0\n",
              "suicides_no             0\n",
              "population              0\n",
              "suicides/100k pop       0\n",
              "country-year            0\n",
              "HDI for year          324\n",
              " gdp_for_year ($)       0\n",
              "gdp_per_capita ($)      0\n",
              "generation              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WmHM3UDsLtBX",
        "colab_type": "text"
      },
      "source": [
        "### Tendência da taxa de suicídio:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7jfaSCv2IzFZ",
        "colab_type": "text"
      },
      "source": [
        "A primeira comparação diz respeito ao número de suicídios cometidos por 100 mil habitantes no Brasil, Russia e a tendência do gráfico em relação à taxa mundial.\n",
        "\n",
        "Para ver a tendência, é melhor sempre usar essa relação por 100 mil, pois a população do país cresceu muito de 1985 para 2015. Se formos comparar em termos absolutos, poderemos tirar conclusões erradas ou distorcidas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OLi25tlnKS-m",
        "colab_type": "code",
        "outputId": "85a21130-788c-419e-91b5-264a745eaaca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "# pegar a media mundial e do Brasil em suicidios\n",
        "years = df_brasil.year.unique()    # pegar os anos para o eixo x\n",
        "years2 = df_russian.year.unique()    # pegar os anos para o eixo x\n",
        "# years2 = df_japan.year.unique()    # pegar os anos para o eixo x\n",
        "# years3 = df_Portugal.year.unique()    # pegar os anos para o eixo x\n",
        "# suicides_brasil_mean = df_brasil.groupby('year')['suicides/100k pop'].mean()\n",
        "# suicides_portugal_mean = df_Portugal.groupby('year')['suicides/100k pop'].mean()\n",
        "# suicides_japan_mean = df_japan.groupby('year')['suicides/100k pop'].mean()\n",
        "suicides_russian_mean = df_russian.groupby('year')['suicides/100k pop'].mean()\n",
        "suicides_world_mean = df.groupby('year')['suicides/100k pop'].mean()\n",
        "\n",
        "# como o Brasil nao tem 2016, vou eliminar do dataframe mundial essa entrada\n",
        "suicides_world_mean.drop(2016, inplace=True)\n",
        "# suicides_japan_mean.drop(2016, inplace=True)\n",
        "# suicides_portugal_mean .drop(2016, inplace=True)\n",
        "\n",
        "# plotar lineplot comparativo entre Brasil e Mundo\n",
        "ax = sns.lineplot(x=years, y=suicides_brasil_mean, label='Brasil')\n",
        "ax = sns.lineplot(x=years2, y=suicides_russian_mean, label='Russia')\n",
        "# ax = sns.lineplot(x=years2, y=suicides_japan_mean, label='Japan')\n",
        "# ax = sns.lineplot(x=years3, y=suicides_portugal_mean, label='Portugal')\n",
        "ax = sns.lineplot(x=years, y=suicides_world_mean, label='Mundo')\n",
        "plt.legend(title=\"Taxa de suicídio\")\n",
        "plt.show()"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n99JaHjAK8Az",
        "colab_type": "text"
      },
      "source": [
        "Uma coisa que é facilmente percebida no gráfico acima é que apesar da taxa de suicídios no Brasil ser menor que a média mundial, ela vem crescendo constantemente ao longo de 30 anos enquanto a da Russia, embora esteja bem acima da média mundial, vem diminuindo ao longo dos anos.\n",
        "\n",
        "A linha laranja teve um pico por volta de 1995, porém reverteu o *slope* e vem caindo ano após ano. Já a linha azul (Brasil) tem um *slope* positivo praticamente ao longo de todo o período analisado."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPU1kfEVK8sm",
        "colab_type": "text"
      },
      "source": [
        "### Faixa etária com maior índice de suícidio\n",
        "\n",
        "Abaixo, eu criei uma tabela dinâmica (*pivot table*) para analisar as 6 faixas etárias em função do ano e do número de suicídios por 100 mil habitantes. \n",
        "\n",
        "O objetivo aqui é identificar qual a faixa etária que tem a maior representatividade entre aqueles que tiram a própria vida, e identificar se houve alguma mudança no perfil ao longo de 30 anos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IR_nZc7bG01Q",
        "colab_type": "code",
        "outputId": "447f6ad7-5303-4a90-db2e-f10a49edfe4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "# criar uma tabela dinâmica\n",
        "table = pd.pivot_table(df_brasil, values='suicides_no', index=['year'], columns=['age'])\n",
        "table2 = pd.pivot_table(df_russian, values='suicides_no', index=['year'], columns=['age'])\n",
        "\n",
        "# reordenar as tableas para deixar em ordem crescente\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",
        "table2 = table2.reindex(column_order, axis=1)\n",
        "\n",
        "# ver as 5 primeiras entradas da pivot table\n",
        "print('------------------Brasil------------------')\n",
        "print(table.head())\n",
        "\n",
        "print('\\n\\n------------------Russia------------------')\n",
        "print(table2.head())"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "------------------Brasil------------------\n",
            "age   5-14 years  15-24 years  25-34 years  35-54 years  55-74 years  75+ years\n",
            "year                                                                           \n",
            "1985        38.0        470.5        530.0        653.5        341.5       80.5\n",
            "1986        31.0        508.0        505.5        651.0        372.0       68.0\n",
            "1987        33.5        502.0        565.5        724.5        422.0       88.5\n",
            "1988        30.0        472.0        557.5        724.0        356.0       93.5\n",
            "1989        31.5        491.5        569.0        692.5        354.5       92.5\n",
            "\n",
            "\n",
            "------------------Russia------------------\n",
            "age   5-14 years  15-24 years  25-34 years  35-54 years  55-74 years  75+ years\n",
            "year                                                                           \n",
            "1989       155.5       1604.5       3841.5       7178.0       4646.0     1535.0\n",
            "1990       156.0       1757.5       3889.0       7492.0       4732.5     1487.0\n",
            "1991       181.5       1813.0       3894.5       7587.5       4746.0     1418.0\n",
            "1992       197.0       2075.5       4474.0       9296.0       5573.5     1345.5\n",
            "1993       215.0       2566.0       5241.0      11709.5       6775.5     1416.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZ8UuFqZLi5T",
        "colab_type": "code",
        "outputId": "740383e7-2cbc-44fb-c7c7-ce46c4352128",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        }
      },
      "source": [
        "# plotar a pivot table das faixas etárias para o Brasil\n",
        "table.plot.bar(stacked=True, figsize=(16,8))\n",
        "plt.legend(title=\"Idade - Brasil\");"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6B1ughmpLium",
        "colab_type": "text"
      },
      "source": [
        "Mesmo visualmente, para o Brasil é fácil identificar que o grupo de pessoas que mais cometem suicídio está entre 35-54 anos. Em segundo lugar, estão pessoas entre 25-34 anos de idade.\n",
        "\n",
        "Juntos, esses dois grupos correspondem a quase 60% dos registros do banco de dados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XjVMn7sD1ggJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "outputId": "9e82968a-6f13-407c-e39c-c9c9065d8cc3"
      },
      "source": [
        "# plotar a pivot table das faixas etárias para a Russia\n",
        "table2.plot.bar(stacked=True, figsize=(16,8))\n",
        "plt.legend(title=\"Idade - Russia\");"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lO0Z6Naz2EKb",
        "colab_type": "text"
      },
      "source": [
        "Para a Russia, como já vimos anteriormente, houve um pico em 1994-95 e a taxa de mortes vem reduzindo ao longo dos últimos 30 anos. E visualmente, também podemos notar que a média do número de mortes entre 55-74 anos de idade tem quase que a mesma quantidade desde 1989.\n",
        "Aqui, podemos identificar que o grupo de pessoas que mais cometem suicídio está entre 35-54 anos. Em segundo lugar, estão pessoas entre 15-24 anos de idade."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QgIVvsPVMiH0",
        "colab_type": "code",
        "outputId": "584c6548-d39a-493a-a31c-e0076575f2f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "# estatística de suicídios por faixa etária\n",
        "print('Brasil')\n",
        "table[['25-34 years']].loc[2015]\n",
        "table.loc[2015] / table.loc[2015].sum()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZHt72icN25_",
        "colab_type": "text"
      },
      "source": [
        "Um outro ponto que vale a pena ressaltar é o aumento de casos entre pessoas acima de 55 anos. Em 1985, as pessoas com mais idade representavam uma pequena fatia do número total.\n",
        "\n",
        "Entretanto, em 2015 é nítido que mesmo para pessoas acima de 75 anos houve um incremento significativo no número de suicídios.\n",
        "\n",
        "Para se inferir a causa dessa mudança de padrão, é necessário se avaliar mais profundamente questões que vão além dos números (como exemplo, fatores qualitativos, momento econômico do país e a cultura dominante de cada época)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yNoS23fV3y-T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "6b41ab29-3d47-4516-cb72-9c10c4cd62b4"
      },
      "source": [
        "# estatística de suicídios por faixa etária\n",
        "print('BRussia')\n",
        "table2[['25-34 years']].loc[2015]\n",
        "table2.loc[2015] / table.loc[2015].sum()"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BRussia\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age\n",
              "5-14 years     0.012183\n",
              "15-24 years    0.206575\n",
              "25-34 years    0.512765\n",
              "35-54 years    0.832124\n",
              "55-74 years    0.493147\n",
              "75+ years      0.221446\n",
              "Name: 2015, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lyrq8U0RDekZ",
        "colab_type": "text"
      },
      "source": [
        "### Taxa de suicídio entre homens e mulheres\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ABgm3pcgRZua",
        "colab_type": "text"
      },
      "source": [
        "Uma outra análise de grande importância é ver a porcentagem dos suicídios entre homens e mulheres.\n",
        "\n",
        "Analisando-se todo o período, o *dataset* utilizado mostrou que aproximadamente 78% dos casos foram cometidos por homens e 22% deles por mulheres. Optou-se por pegar a média dos 30 anos, pois não houve mudança significativa desse comportamento durante o período."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-qsgukyP1MB",
        "colab_type": "code",
        "outputId": "f93779e8-bbe2-4c80-faaf-f5398c376c2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        }
      },
      "source": [
        "# extrair valores entre homens e mulheres\n",
        "print('Brasil')\n",
        "homens_mulheres = df_brasil.groupby('sex').suicides_no.sum() / df_brasil.groupby('sex').suicides_no.sum().sum()\n",
        "\n",
        "# plotar o gráfico de pizza\n",
        "plt.pie(homens_mulheres, labels=['mulheres', 'homens'], autopct='%1.1f%%', shadow=True);"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "akPnEdyr4JFB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "4eb5b49f-4d9d-42e3-b163-b165b849178a"
      },
      "source": [
        "# extrair valores entre homens e mulheres\n",
        "print('Brasil')\n",
        "homens_mulheres = df_russian.groupby('sex').suicides_no.sum() / df_russian.groupby('sex').suicides_no.sum().sum()\n",
        "\n",
        "# plotar o gráfico de pizza\n",
        "plt.pie(homens_mulheres, labels=['mulheres', 'homens'], autopct='%1.1f%%', shadow=True);"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Brasil\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ixz-drYDQBwb",
        "colab_type": "text"
      },
      "source": [
        "### Correlações entre o PIB, IDH e número de suicídios\n",
        "\n",
        "Criando uma matriz de correlação e plotando um *heatmap*, infere-se que o aumento no PIB per capita não diminuiu o número de suicídios por 100 mil habitantes. Na verdade, ele se manteve estável, contrariando o senso comum da maioria das pessoas.\n",
        "\n",
        "Em relação ao IDH, como foi mencionado anteriormente, há muitos valores ausentes nas células, o que pode dar uma interpretação incorreta ou com viés.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jcd0QdNySOVa",
        "colab_type": "code",
        "outputId": "c9fb44b0-2de4-4dc0-e89f-02df4262b57d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        }
      },
      "source": [
        "# plotar heatmap\n",
        "corr = df_brasil.corr()\n",
        "sns.heatmap(corr, cmap='RdBu', fmt='.2f', square=True, linecolor='white', annot=True);"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ddUo5jicTjaI",
        "colab_type": "text"
      },
      "source": [
        "## Prevenção do suicídio\n",
        "O conjunto de dados usado aqui é simplificado, porém ideal para uma abordagem inicial, para se criar uma consciência situacional a respeito do tema.\n",
        "\n",
        "Há diversas iniciativas dentro do campo da Inteligência Artificial visando não apenas a conscientização, mas também prevenção de suicídios. [Uma das mais populares diz respeito à análise de postagens em redes sociais](https://qz.com/1367197/machines-know-when-someones-about-to-attempt-suicide-how-should-we-use-that-information/), onde algoritmos de *Machine Learning* são capazes de identificar potenciais suicidas e alertar outras pessoas.\n",
        "\n",
        "Lembre-se que não é frescura e que ninguém está querendo aparecer. Na verdade, a gente nunca tem a mínima ideia do que a outra pessoa está passando. Qualquer atitude que você tenha que possa contribuir para a prevenção do suicídio, pode significar uma vida a mais ;)\n"
      ]
    }
  ]
}