diff --git a/README.md b/README.md index 6f06bd0..940deb3 100644 --- a/README.md +++ b/README.md @@ -1,104 +1,57 @@ -

- logo reprograma -

- -# Tema da Aula - -Turma Online 34 | Python | Semanas 17 e 18 | 2024 | [Daniele Junior](https://travatech.com.br?router=danijr) - -### Instruções -Antes de começar, vamos organizar nosso setup. -* Fork esse repositório -* Clone o fork na sua máquina (Para isso basta abrir o seu terminal e digitar `git clone url-do-seu-repositorio-forkado`) -* Entre na pasta do seu repositório (Para isso basta abrir o seu terminal e digitar `cd nome-do-seu-repositorio-forkado`) -* [Add outras instruções caso necessário] - -### Resumo -O que veremos na aula de hoje? -* [Slide Semana 17](https://docs.google.com/presentation/d/1axo2Dlm0Hx35ahKdZW6s-UAdG61L41QXdete8ZcQV0w/edit?usp=sharing) -* Slide Semana 18 - -* [Escolhendo uma fonte de dados](#Escolhendoumafontededados) -* Análise exploratória -* Criando uma história com dados - -## Conteúdo - -### O que é um projeto de análise de dados? -Nesse ponto vocês já aprenderam que ter dados não é a mesma coisa que ter informação. -**Dados:** são elementos brutos e não processados, como números, palavras, ou símbolos que precisam ser interpretados para se tornarem úteis. -**Informação:** é o resultado do processamento, organização e interpretação dos dados, fornecendo significado e contexto para tomar decisões ou entender situações. -Assim, dados são a matéria-prima da informação, que é o produto final após análise e interpretação dos dados. - -Por isso a importância de nós contarmos uma história estruturada a partir dos dados que conseguimos coletar. E é exatamente sobre isso, que se trata um projeto de análise de dados: **gerar informação útil a partir da construção de uma perspectiva contextualizada!** - -Então aqui vão algumas perguntas gerais que devemos nos fazer ao iniciar um projeto como esse: - -- **Conteúdo** - - O que eu quero informar? -- **Público** - - Para quem eu estou contanto essa história? Com quem vou compartilhar essa informação? -- **Transformação** - - Por que essa informação é relevante? - -Ok, as perguntas são importantes, - -MAS POR ONDE COMEÇAR?! - -### Escolhendo uma fonte de dados - -#### O caminho comum -Se você já fez algum tipo de pesquisa acadêmica (TCC, Iniciação Científica, etc) você certamente está familiarizado com esse processo, pois tudo começa com a escolha de um TEMA, seguindo para a definição do PROBLEMA, que em seguida é desdobrado em PERGUNTAS, que irão guiar a COLETA DE DADOS. - -1. Delimitação do Tema -2. Definição do Problema -3. Desenvolvimento de Perguntas -4. Coleta de Dados - -#### O caminho que iremos seguir -Porque esse projeto é um exercício e encontrar os dados ideais para responder às nossas perguntas pode se tornar um trabalho extremamente complexo... - -Nós iremos fazer um caminho um pouco diferente e a partir de um tema de interesse, escolher uma base e então pensar quais perguntas podem ser respondidas a partir dela. - -O QUE TAMBÉM É SUPER VÁLIDO! E PODE RENDER DESCOBERTAS INCRÍVEIS! - - * **Escolha do tema** - - No primeiro momento você deve escolher qual assunto gostaria de abordar. Pense em um tema atual, relevante e até onde você vai aprofundar a análise. Lembre-se, não adianta abraçar o mundo sozinho, você precisa focar e entregar o melhor resultado possível, então trabalhe na delimitação do Tema! Quais são os recortes possíveis dentro do universo escolhido? - - #Dica: Dê prioridade para algo que você goste, se interesse, tenha afinidade ou conhecimento na área. - - * **Escolha da Base de Dados** - - [Algumas opções de Bases de Dados](#base-de-dados) - -* **Definindo nossas perguntas** - - O que eu quero tentar responder? VAMOS AO [BRAINSTORM](#material-da-aula)! - -*** - -### Material da aula - -* [Slides](https://docs.google.com/presentation/d/1axo2Dlm0Hx35ahKdZW6s-UAdG61L41QXdete8ZcQV0w/edit?usp=sharing) - -### Links Úteis -- [Documentação Pandas](https://pandas.pydata.org/docs/user_guide/index.html#user-guide) -- [Introdução ao Pandas](https://medium.com/tech-grupozap/introdu%C3%A7%C3%A3o-a-biblioteca-pandas-89fa8ed4fa38) -- [Análise Exploratória de Dados I](https://escoladedados.org/tutoriais/analise-exploratoria-de-dados/) -- [Análise Exploratória de Dados II](https://www.alura.com.br/artigos/analise-exploratoria) -- [Storytelling com Dados](https://medium.com/resumos-resenhas/storytelling-com-dados-resumo-fd63ebe4f704) -- [Markdown Cheastsheet](https://www.ibm.com/docs/en/watson-studio-local/1.2.3?topic=notebooks-markdown-jupyter-cheatsheet) - - #### Base de Dados -- [Kaggle](https://www.kaggle.com/datasets) -- [IBGE](https://ces.ibge.gov.br/base-de-dados/links-base-de-dados.html) -- [Brasil.io](https://brasil.io/datasets/) -- [Gov.br](https://dados.gov.br/dados/conjuntos-dados) -- [Nosso Mundo em Dados](https://ourworldindata.org/charts) - -

-Desenvolvido com :purple_heart: -

- +# Análise de Dados do Disque 100: Discriminação de Religiões Afro no Brasil (2020-2023) + +## Sumário +- [Descrição do Projeto](#descrição-do-projeto) +- [Objetivos](#objetivos) +- [Dados](#dados) +- [Metodologia](#metodologia) +- [Resultados](#resultados) +- [Tecnologias Utilizadas](#tecnologias-utilizadas) +- [Como Executar o Projeto](#como-executar-o-projeto) +- [Conclusão](#conclusão) +- [Referências](#referências) + +## Descrição do Projeto +Este projeto visa analisar os dados coletados pelo Disque 100 sobre discriminação de religiões afro no Brasil entre os anos de 2020 e 2023. Através dessa análise, buscamos identificar padrões, tendências e propor melhorias com base nas informações encontradas. + +## Objetivos +- Analisar as denúncias relacionadas à discriminação de religiões afro no Brasil. +- Identificar as regiões mais afetadas por essas discriminações. +- Investigar possíveis correlações entre as denúncias e variáveis demográficas. +- Propor recomendações para políticas públicas e ações de conscientização. + +## Dados +Os dados utilizados neste projeto foram extraídos do Disque 100, que é um serviço de denúncias sobre violações de direitos humanos. As informações analisadas incluem: +- Tipo de discriminação (relacionada a religiões afro) +- Localização das denúncias (UF e Município) +- Gênero das vítimas +- Link Disque 100: https://www.gov.br/mdh/pt-br/acesso-a-informacao/dados-abertos/disque100/ + +Os dados foram filtrados para incluir somente registros pertinentes ao tema da discriminação religiosa entre 2020 e 2023. + +## Metodologia +- **Limpeza de Dados**: Os dados brutos foram limpos e organizados para remover duplicatas e entradas inválidas. +- **Análise Exploratória**: Realizamos uma análise exploratória para entender melhor as características dos dados, incluindo distribuições e correlações. +- **Visualizações**: Gráficos e tabelas foram criados para ilustrar os resultados da análise, permitindo uma melhor interpretação dos dados. + +## Resultados +- Identificação das UFs com maior número de denúncias. +- Comparação entre denúncias por gênero. +- Propostas de ações para combater a discriminação. +- Dashboard no Tableau com as análises do projeto: https://public.tableau.com/views/PROJETOFINAL_17287731222110/Painel1?:language=pt-BR&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link +- Slides da apresentação com o contexto, principais pontos e conclusões do projeto: https://www.canva.com/design/DAGTc2O7Iv8/nossToGf4k_r22yjHjecIQ/edit?utm_content=DAGTc2O7Iv8&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton + +## Tecnologias Utilizadas +- **Python** +- **Pandas** (manipulação de dados) +- **Matplotlib** e **Seaborn** (visualização de dados) +- **Tableau** +- **Jupyter Notebook** (para desenvolvimento e apresentação do projeto) +- Bibliotecas para manipulação de dados e visualização + +## Conclusão +A análise dos dados do Disque 100 sobre discriminação de religiões afro no Brasil entre 2020 e 2023 revela informações valiosas que podem auxiliar na criação de políticas públicas e programas de conscientização. Esperamos que os resultados deste projeto contribuam para a luta contra a discriminação e promoção da igualdade. + +## Referências +Disque 100 - Direitos Humanos (https://www.gov.br/mdh/pt-br/acesso-a-informacao/dados-abertos/disque100/) diff --git a/material/analise-exploratoria/analise.ipynb b/material/analise-exploratoria/analise.ipynb deleted file mode 100644 index 1cce302..0000000 --- a/material/analise-exploratoria/analise.ipynb +++ /dev/null @@ -1,22 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#Utilizar as bibliotecas de Python aprendidas em aula (pandas, matplotlib, seaborn, etc);\n", - "#Trazer um notebook estruturado e organizado com o uso de Markdown. O uso de textos no notebook é altamente incentivado);\n", - "#Mínimo de 3 visualizações que ajudem a sumarizar os resultados da sua análise." - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/material/analise-exploratoria/projeto_final.ipynb b/material/analise-exploratoria/projeto_final.ipynb new file mode 100644 index 0000000..1c9a554 --- /dev/null +++ b/material/analise-exploratoria/projeto_final.ipynb @@ -0,0 +1,866 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "VTAhmTUOODwm" + }, + "source": [ + "# **Importação, tratamento e manipulação inicial dos dados:**\n", + "Iniciamos nosso projeto realizando as importações necessárias. Aqui estamos trazendo bibliotecas que nos permitem manipular dados e criar gráficos, além de conectar ao Google Drive para acessar os arquivos CSV com as denúncias." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "v3MPlZUJZt_Y" + }, + "outputs": [], + "source": [ + "import matplotlib as plt\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import re\n", + "from google.colab import drive\n", + "from google.colab import files\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IbfC_7r2OSQB" + }, + "source": [ + "Montamos o Google Drive para acessar os arquivos de dados; Ao montar o Google Drive, nós garantimos acesso aos arquivos diretamente da nuvem, o que facilita o trabalho com grandes datasets. Nesse mesmo bloco de código nos preocupamos em definir os caminhos para cada arquivo CSV correspondente aos semestres de 2020 a 2023, uma vez que essa etapa organiza os arquivos que vamos utilizar, o que facilita o carregamento futuro." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gAA9K-xrBPe1", + "outputId": "d6248060-7c09-4f47-ea93-2d9934ad3a51" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mounted at /content/drive\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":17: DtypeWarning: Columns (20,21,22,25,41,42,43,46,52,56) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " segundo_semestre_2022 = pd.read_csv(caminho_2022_2, sep=';')\n", + ":18: DtypeWarning: Columns (34) have mixed types. Specify dtype option on import or set low_memory=False.\n", + " primeiro_semestre_2023 = pd.read_csv(caminho_2023_1, sep=';')\n" + ] + } + ], + "source": [ + "drive.mount('/content/drive')\n", + "\n", + "caminho_2020_1 = '/content/drive/MyDrive/Projeto_final_ON34/disque100-primeiro-semestre-2020.csv'\n", + "caminho_2020_2 = '/content/drive/MyDrive/Projeto_final_ON34/disque100-segundo-semestre-2020.csv'\n", + "caminho_2021_1 = '/content/drive/MyDrive/Projeto_final_ON34/disque100-primeiro-semestre-2021.csv'\n", + "caminho_2021_2 = '/content/drive/MyDrive/Projeto_final_ON34/disque100-segundo-semestre-2021.csv'\n", + "caminho_2022_1 = '/content/drive/MyDrive/Projeto_final_ON34/disque100-primeiro-semestre-2022.csv'\n", + "caminho_2022_2 = '/content/drive/MyDrive/Projeto_final_ON34/disque100-segundo-semestre-2022.csv'\n", + "caminho_2023_1 = '/content/drive/MyDrive/Projeto_final_ON34/disque100-primeiro-semestre-2023 (1).csv'\n", + "caminho_2023_2 = '/content/drive/MyDrive/Projeto_final_ON34/disque100-segundo-semestre-2023.csv'\n", + "\n", + "primeiro_semestre_2020 = pd.read_csv(caminho_2020_1, sep=';')\n", + "segundo_semestre_2020 = pd.read_csv(caminho_2020_2, sep=';')\n", + "primeiro_semestre_2021 = pd.read_csv(caminho_2021_1, sep=';')\n", + "segundo_semestre_2021 = pd.read_csv(caminho_2021_2, sep=';')\n", + "primeiro_semestre_2022 = pd.read_csv(caminho_2022_1, sep=';')\n", + "segundo_semestre_2022 = pd.read_csv(caminho_2022_2, sep=';')\n", + "primeiro_semestre_2023 = pd.read_csv(caminho_2023_1, sep=';')\n", + "segundo_semestre_2023 = pd.read_csv(caminho_2023_2, sep=';')\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-_rW7SAS5T00" + }, + "source": [ + " Inserimos uma coluna com o ano em cada dataset, facilitando a análise temporal; Aqui criamos uma nova coluna chamada 'ano' para sabermos de qual ano cada registro de denúncia pertence, o que nos ajudará a analisar a variação das denúncias ao longo do tempo." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "eWlzxWzyjT-3" + }, + "outputs": [], + "source": [ + "primeiro_semestre_2020['ano'] = 2020" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "J_VIXdmW1Fiq" + }, + "outputs": [], + "source": [ + "segundo_semestre_2020['ano'] = 2020" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "UvGXHJumm4wh" + }, + "outputs": [], + "source": [ + "primeiro_semestre_2021['ano'] = 2021\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "_9lQZVlSm9wX" + }, + "outputs": [], + "source": [ + "segundo_semestre_2021['ano'] = 2021\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "F4UCI0wdnCN3" + }, + "outputs": [], + "source": [ + "primeiro_semestre_2022['ano'] = 2022\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "mMiaOAVtnDuV" + }, + "outputs": [], + "source": [ + "segundo_semestre_2022['ano'] = 2022" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "gh81sVhEnHkB" + }, + "outputs": [], + "source": [ + "primeiro_semestre_2023['ano'] = 2023\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "Nq4VJ72DnI-p" + }, + "outputs": [], + "source": [ + "segundo_semestre_2023['ano'] = 2023" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7n5ividV5hrT" + }, + "source": [ + "Para nos certificarmos de que os arquivos estão sendo carregados corretamentes e facilitar nossa análise, concatenamos os DataFrames de cada semestre, criando um dataset consolidado, com todas as denúncias entre 2020 e 2023." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CQb0AuflakoK", + "outputId": "1d12e2fa-79b0-411d-92a0-38a048e50f2e" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2020, 2021, 2022, 2023])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "historico_denuncias_bruto = pd.concat([primeiro_semestre_2020, segundo_semestre_2020, primeiro_semestre_2021, segundo_semestre_2021, primeiro_semestre_2022, segundo_semestre_2022, primeiro_semestre_2023, segundo_semestre_2023])\n", + "historico_denuncias_bruto['ano'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Jkmj0npmQupV" + }, + "source": [ + "Já pensando na resposta da primeira pergunta, resolvemos verificar quais religiões estão sendo capturadas nos dados, para conferir se temos dados somente de vítimas de religiões de matriz afro, ou se precisamos filtrar mais." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kyUPgsNR2NZy", + "outputId": "7603d0ef-49d0-4b20-cc65-07df0c7aebe7" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([nan, 'N/D', 'CATÓLICA APOSTÓLICA ROMANA', 'ESPIRITISMO',\n", + " 'EVANGELHO QUADRANGULAR', 'IGREJA DEUS É AMOR',\n", + " 'ADVENTISTA DO SÉTIMO DIA', 'CONGREGAÇÃO CRISTÃ NO BRASIL',\n", + " 'ASSEMBLEIA DE DEUS', 'OUTROS PENTECOSTAIS/NEOPENTECOSTAIS',\n", + " 'SEM RELIGIÃO', 'PRESBITERIANA', 'OUTRAS RELIGIÕES', 'BATISTA',\n", + " 'CATÓLICA ORTODOXA', 'CANDOMBLÉ', 'BUDISMO', 'UMBANDA', 'LUTERANA',\n", + " 'UNIVERSAL DO REINO DE DEUS', 'TESTEMUNHAS DE JEOVÁ', 'MESSIÂNICA',\n", + " 'OUTRAS DE MATRIZ AFRICANA', 'BAHAÍSMO', 'JUDAÍSMO', 'METODISTA',\n", + " 'TAOÍSMO', 'MÓRMONS', 'SEICHO-NO-IÊ', 'ISLAMISMO', 'HARE',\n", + " 'CRENÇAS INDÍGENAS', 'HINDUÍSMO', 'XINTOÍSMO', 'KRISHNA',\n", + " 'UMBANDA E CANDOMBLÉ'], dtype=object)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "historico_denuncias_bruto['Religião da vítima'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zdVsRO_lROwc" + }, + "source": [ + "Como conferimos, há mais religiões sendo puxadas do que a que pretendemos tratar. Aqui, vamos isolar os casos que realmente envolvem religiões de matriz afro, nosso foco. Esse filtro garante que as análises sejam direcionadas para entender o cenário específico das religiões afro-brasileiras." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UAEoMrAF2Ti4", + "outputId": "cfa0cded-08d5-4324-8b79-dad9d92027fa" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([nan, 'CANDOMBLÉ', 'UMBANDA', 'OUTRAS DE MATRIZ AFRICANA',\n", + " 'UMBANDA E CANDOMBLÉ'], dtype=object)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "historico_denuncias = historico_denuncias_bruto.loc[~historico_denuncias_bruto['Religião da vítima'].isin(['ESPIRITISMO', 'BATISTA', 'N/D', 'CATÓLICA APOSTÓLICA ROMANA','EVANGELHO QUADRANGULAR', 'IGREJA DEUS É AMOR','ADVENTISTA DO SÉTIMO DIA', 'CONGREGAÇÃO CRISTÃ NO BRASIL','ASSEMBLEIA DE DEUS', 'OUTROS PENTECOSTAIS/NEOPENTECOSTAIS','SEM RELIGIÃO', 'PRESBITERIANA', 'OUTRAS RELIGIÕES', 'BATISTA','CATÓLICA ORTODOXA','BUDISMO', 'LUTERANA', 'UNIVERSAL DO REINO DE DEUS', 'TESTEMUNHAS DE JEOVÁ', 'MESSIÂNICA', 'BAHAÍSMO', 'JUDAÍSMO', 'METODISTA', 'TAOÍSMO', 'MÓRMONS', 'SEICHO-NO-IÊ', 'ISLAMISMO', 'HARE', 'CRENÇAS INDÍGENAS', 'HINDUÍSMO', 'XINTOÍSMO', 'KRISHNA'])]\n", + "historico_denuncias['Religião da vítima'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pmH64ptn6DN7" + }, + "source": [ + "Em seguida, visualizamos os dados faltantes (vazios) em cada coluna, ordenando para facilitar a análise. Aqui vemos se há dados faltantes em algumas colunas. Isso é importante porque, ao fazer análises, dados faltantes podem distorcer os resultados, e precisamos entender o que pode ser feito (como remover ou tratar esses valores)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 458 + }, + "id": "HxEARZ4OdIKv", + "outputId": "ca8afd63-004a-4978-8470-d474e4f7a433" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "ano 0\n", + "Denunciante 303\n", + "UF 1514\n", + "Município 2168\n", + "Canal_de_atendimento 8474\n", + " ... \n", + "Vínculo Órgão\\PJ do suspeito 2458275\n", + "Deficiência_relacionada_a_doença_rara 2458376\n", + "Deficiência_relacionada_a_doença_rara_suspeito 2458379\n", + "Grau_de_instrução_do_suspeito 2458393\n", + "Vínculo_Órgão_PJ_do_suspeito 2458396\n", + "Length: 128, dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "historico_denuncias.isnull().sum().sort_values(ascending=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2LKOyTaT4WGi" + }, + "source": [ + "As colunas selecionadas são as que vão nos ajudar a responder perguntas-chave do projeto, como:\n", + "- Como a quantidade de denúncias variou ao longo dos anos?;\n", + "- Quais estados apresentam a maior quantidade de denúncias?;\n", + "- Qual o perfil demográfico das vítimas?;\n", + "\n", + "Após conferir quais as colunas disponíveis, separamos aquelas que poderiam responder às perguntas, como é possível ver em seguida:\n", + "\n", + "* Coluna selecionadas para a pergunta 1 = Data_de_cadastro;\n", + "* Colunas selecionadas para a pergunta 2 = UF, Município;\n", + "* Colunas selecionadas para a pergunta 3 = Gênero_da_vítima, Faixa_etária_da_vítima, Raça_Cor_da_vítima.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7TUx4fZoRm3h" + }, + "source": [ + "#**Pergunta 1: Como a quantidade de denúncias variou ao longo dos anos?**\n", + "Agora vamos analisar a quantidade de denúncias por ano. Isso vai nos ajudar a identificar tendências. Será que as denúncias aumentaram ou diminuíram com o passar dos anos?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kREGj3zBoC1Z", + "outputId": "bc80e62c-0abc-42f8-b1a9-71ef8453292f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ano Quantidade de Denúncias\n", + "0 2020 138440\n", + "1 2021 4054\n", + "2 2022 941829\n", + "3 2023 1374073\n" + ] + } + ], + "source": [ + "denuncias_por_ano = historico_denuncias.groupby('ano').size().reset_index(name='Quantidade de Denúncias')\n", + "print(denuncias_por_ano)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iQ7mkLzxR4fY" + }, + "source": [ + "Analisando a variação por ano, conseguimos ver se o problema de perseguição às religiões afro-brasileiras está piorando ou se as políticas públicas e a conscientização da sociedade estão tendo algum efeito positivo.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VKU_7wmC38Yn", + "outputId": "455f0cbb-d6f4-42e7-ffe7-bc86fe08e527" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ano Quantidade de Denúncias\n", + "0 2020 138440\n", + "1 2021 4054\n", + "2 2022 941829\n", + "3 2023 1374073\n" + ] + } + ], + "source": [ + "print(denuncias_por_ano)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BWDCK2sCHf6w" + }, + "source": [ + "Transformamos em csv para fazer o projeto no Tableau." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ML2fn3Sp4FEo" + }, + "outputs": [], + "source": [ + "denuncias_por_ano.to_csv('denuncias_por_ano.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b9swKkxU3RuX" + }, + "source": [ + "# **Pergunta 2: Quais estados apresentam a maior quantidade de denúncias?**\n", + "Verificamos onde a maior parte das denúncias está acontecendo. Isso nos ajuda a entender se há uma concentração geográfica de perseguição religiosa e, se sim, onde seria importante focar esforços de combate à intolerância." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lmIobXh23Zdz", + "outputId": "44c34c97-c47d-4f78-f109-5c4e48e51c8e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['MA' 'SP' 'PA' 'PE' 'MG' 'CE' 'BA' 'RJ' 'RS' 'SC' 'ES' 'PI' 'PR' 'DF'\n", + " 'GO' 'AL' 'AM' 'RN' 'RO' 'AC' 'MS' 'PB' 'AP' 'MT' 'RR' 'SE' 'N/D' 'TO'\n", + " 'DENUNCIANTE NÃO SOUBE INFORMAR' nan 'ATENDIMENTO INTERROMPIDO']\n" + ] + } + ], + "source": [ + "print(historico_denuncias['UF'].unique())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b-u0jtVDSawd" + }, + "source": [ + "Ordenamos os estados pelo número de denúncias em ordem decrescente, exibimos os resultados, e criamos gráfico de barras mostrando as denúncias por estado. Este gráfico nos dará uma visão visual clara de quais estados precisam de mais atenção em relação às denúncias de perseguição religiosa." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "AJzitFXtpK5F", + "outputId": "120ee4c2-5a93-4e6c-a5ea-e5d406223d72" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tabela com as regiões com mais denúncias (do maior para o menor):\n", + " UF Quantidade de Denúncias\n", + "0 SP 659714\n", + "1 RJ 331437\n", + "2 MG 273123\n", + "3 RS 142206\n", + "4 BA 113865\n", + "5 PR 94855\n", + "6 PE 92130\n", + "7 SC 85059\n", + "8 CE 80790\n", + "9 GO 65320\n", + "10 DF 51883\n", + "11 RN 49810\n", + "12 AM 47947\n", + "13 ES 47658\n", + "14 PA 43875\n", + "15 MA 42120\n", + "16 MS 37710\n", + "17 PB 34863\n", + "18 PI 30837\n", + "19 AL 30434\n", + "20 SE 25021\n", + "21 MT 18333\n", + "22 DENUNCIANTE NÃO SOUBE INFORMAR 17785\n", + "23 RO 12787\n", + "24 TO 11036\n", + "25 AC 6402\n", + "26 AP 4296\n", + "27 RR 3446\n", + "28 ATENDIMENTO INTERROMPIDO 926\n", + "29 N/D 475\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "denuncias_por_uf = historico_denuncias.dropna(subset=['UF', 'Município'])['UF'].value_counts().reset_index()\n", + "\n", + "denuncias_por_uf.columns = ['UF', 'Quantidade de Denúncias']\n", + "\n", + "denuncias_por_uf = denuncias_por_uf.sort_values(by='Quantidade de Denúncias', ascending=False)\n", + "\n", + "print(\"Tabela com as regiões com mais denúncias (do maior para o menor):\")\n", + "print(denuncias_por_uf)\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.bar(denuncias_por_uf['UF'], denuncias_por_uf['Quantidade de Denúncias'], color='skyblue')\n", + "plt.title('Quantidade de Denúncias por UF (do maior para o menor)')\n", + "plt.xlabel('UF')\n", + "plt.ylabel('Quantidade de Denúncias')\n", + "plt.xticks(rotation=45)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ch3SDKX_SwgL" + }, + "source": [ + "Esse gráfico pode revelar estados específicos onde há uma maior intolerância religiosa contra religiões afro-brasileiras, indicando áreas para campanhas de conscientização e ação pública.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "y736LgmmHpRa" + }, + "source": [ + "Transformamos em csv para fazer o projeto no Tableau." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "09-f-Lxr42Sg" + }, + "outputs": [], + "source": [ + "denuncias_por_uf.to_csv('denuncias_por_uf.csv', index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TJpok2Rc6pfQ" + }, + "source": [ + "# **Pergunta 3: Qual é o perfil demográfico das vítimas?? Existe alguma tendência?**\n", + "Em seguida, olhamos para o perfil das vítimas: idade, gênero e raça e plotamos seus gráficos. Isso nos dá uma ideia de quais grupos estão mais vulneráveis." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "XkivKsOnW5Y8" + }, + "outputs": [], + "source": [ + "historico_denuncias_demografia = historico_denuncias.dropna(subset=['Gênero_da_vítima', 'Faixa_etária_da_vítima', 'Raça_Cor_da_vítima'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 912 + }, + "id": "CK45VzeIW80K", + "outputId": "88692b44-95c3-414e-97b9-74afea5a9883" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Denúncias por gênero:\n", + " Gênero_da_vítima Quantidade de Denúncias\n", + "0 FEMININO 1238512\n", + "1 MASCULINO 794040\n", + "2 NÃO SE APLICA - VÍTIMA COMUNIDADE/FAMÍLIA 93576\n", + "3 NÃO INFORMADO 35891\n", + "4 INTERSEXO 1619\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "denuncias_por_genero = historico_denuncias_demografia['Gênero_da_vítima'].value_counts().reset_index()\n", + "denuncias_por_genero.columns = ['Gênero_da_vítima', 'Quantidade de Denúncias']\n", + "\n", + "print(\"Denúncias por gênero:\")\n", + "print(denuncias_por_genero)\n", + "\n", + "denuncias_por_genero.plot(kind='bar', x='Gênero_da_vítima', y='Quantidade de Denúncias', legend=False, color='skyblue')\n", + "plt.title('Denúncias por Gênero')\n", + "plt.ylabel('Quantidade de Denúncias')\n", + "plt.xlabel('Gênero')\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 725 + }, + "id": "JKLv70IBhVle", + "outputId": "e3100aa2-38bc-4093-b343-3a1512f1b2f3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Denúncias por raça/cor:\n", + " Raça/Cor Quantidade de Denúncias\n", + "0 BRANCA 896942\n", + "1 PARDA 764532\n", + "2 NÃO INFORMADO 250054\n", + "3 PRETA 236441\n", + "4 AMARELA 8570\n", + "5 INDÍGENA 7098\n", + "6 SEM DECLARAÇÃO 1\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "denuncias_por_raca = historico_denuncias_demografia['Raça_Cor_da_vítima'].value_counts().reset_index()\n", + "denuncias_por_raca.columns = ['Raça/Cor', 'Quantidade de Denúncias']\n", + "\n", + "print(\"Denúncias por raça/cor:\")\n", + "print(denuncias_por_raca)\n", + "\n", + "denuncias_por_raca.plot(kind='bar', x='Raça/Cor', y='Quantidade de Denúncias', legend=False, color='lightcoral')\n", + "plt.title('Denúncias por Raça/Cor')\n", + "plt.ylabel('Quantidade de Denúncias')\n", + "plt.xlabel('Raça/Cor')\n", + "plt.xticks(rotation=45)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I83Ezsp3TO8f" + }, + "source": [ + "Ao entender o perfil das vítimas, conseguimos identificar quais grupos estão mais expostos à violência religiosa, permitindo ações mais direcionadas e campanhas de apoio específicas." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2V-xJ-Z-HzuX" + }, + "source": [ + "Ao fim, transformamos os blocos de código restantes em csv para fazer o projeto no Tableau." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FzlPBkXYfpVU" + }, + "outputs": [], + "source": [ + "denuncias_por_genero.to_csv('/content/denuncias_por_genero.csv', index=False)\n", + "denuncias_por_faixa_etaria.to_csv('/content/denuncias_por_faixa_etaria.csv', index=False)\n", + "denuncias_por_raca.to_csv('/content/denuncias_por_raca.csv', index=False)\n", + "\n", + "files.download('/content/denuncias_por_genero.csv')\n", + "files.download('/content/denuncias_por_faixa_etaria.csv')\n", + "files.download('/content/denuncias_por_raca.csv')\n", + "\n" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/material/informacoes-projeto.md b/material/informacoes-projeto.md new file mode 100644 index 0000000..452a11b --- /dev/null +++ b/material/informacoes-projeto.md @@ -0,0 +1,26 @@ +# Análise de Dados do Disque 100: Perseguição a Religiões de Matriz Afro no Brasil (2020-2023) + +Este projeto consiste na análise dos dados de denúncias feitas ao Disque 100, com foco em discriminação e perseguição às religiões de matriz africana no Brasil entre os anos de 2020 e 2023. O objetivo principal deste projeto é identificar padrões, tendências e características demográficas das vítimas de intolerância religiosa, bem como mapear os estados com as maiores taxas de denúncia. A análise visa fornecer insights que ajudem a compreender o alcance e a gravidade dessa forma de discriminação, contribuindo para a conscientização e o desenvolvimento de políticas públicas de combate ao racismo religioso. + +Os dados utilizados foram extraídos do portal de [Dados Abertos do Disque 100](https://www.gov.br/mdh/pt-br/acesso-a-informacao/dados-abertos/disque100/), e foram filtrados para incluir apenas registros pertinentes ao tema da discriminação religiosa no período de 2020 a 2023. + +## Objetivos gerais e específicos do projeto + +### Objetivo geral: +- Analisar as denúncias de intolerância religiosa contra religiões de matriz afro registradas no Disque 100 entre 2020 e 2023, identificando tendências e padrões relevantes. + +### Objetivos específicos: +- Avaliar como a quantidade de denúncias variou ao longo dos anos. +- Identificar os estados brasileiros com as maiores taxas de denúncias de discriminação religiosa. +- Analisar a demografia das vítimas, considerando gênero e raça/cor, para entender melhor o perfil dos alvos dessa discriminação. + +## Bases escolhidas + +- **Disque 100 - Denúncias de Discriminação Religiosa (2020-2023)**: Base de dados oficial do Disque 100 contendo informações sobre denúncias de discriminação religiosa, incluindo cenário de violação, motivação, UF (estado), município, e dados demográficos das vítimas. Esses dados foram filtrados para incluir somente denúncias relacionadas à discriminação religiosa. + +## Ferramentas utilizadas + +- **Python (Pandas)**: Utilizado para tratamento, limpeza e análise exploratória dos dados. +- **Tableau**: Usado para criar visualizações interativas e gráficos que facilitam a compreensão das análises realizadas. +- **Google Colab**: Ambiente de desenvolvimento utilizado para o processamento e análise de dados. +- **VSCode**: Ferramenta auxiliar para manipulação e organização de código Python. diff --git a/material/nome-projeto.md b/material/nome-projeto.md deleted file mode 100644 index 55c478b..0000000 --- a/material/nome-projeto.md +++ /dev/null @@ -1,12 +0,0 @@ -## Contexto -Esse projeto consiste na análise de xxxxxx. O objetivo desse projeto é xxxxxxxxx. -Para desenvolver esse projeto, desenvolvemos uma análise exploratória de dados xxxxxxx e utilizamos o Tableau para gerar a visualização das nossas análises. - -### Objetivos gerais e específicos do projeto - -### Bases escolhidas - -- Base 1 (fonte) -- Base 2 (fonte) - -## Ferramentas utilizadas \ No newline at end of file