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AnoPotencial DoadorPotencial Doador (PMP)Doador EfetivoDoador Efetivo (PMP)Percentual de EfetivaçãoEntrevista FamiliarNegativa FamiliarNegativa Familiar (%)
2201317755,94413,924,9%27914652,3%
3201417555,24413,925,1%23310545,1%
4201515746,13610,622,9%1447954,9%
5201615244,23911,325,7%975354,6%
6201717349,84713,527,2%1246653,2%
7201815945,3329,120,1%1017170,3%
8201921762,45214,924,0%1267156,3%
9202018853,6246,812,8%1006464,0%
10202120758,6195,49,2%1076762,6%
11202223164,93610,115,6%1318867,2%
12202322367,5329,714,3%1348966,4%
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Evolução 2001 - 2023 - Table 1 (2).csv', index=False)" + ], + "metadata": { + "id": "NgW2sVSlUeN1" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#fazer dowload\n", + "from google.colab import files\n", + "files.download('Relatório de Doação (Rio Grande do Norte) - Evolução 2001 - 2023 - Table 1 (2).csv')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + }, + "id": "SgU4a3shU4iD", + "outputId": "b7c9982a-3152-4d5f-c9f4-63c72a411581" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "\n", + " async function download(id, filename, size) {\n", + " if (!google.colab.kernel.accessAllowed) {\n", + " return;\n", + " }\n", + " const div = document.createElement('div');\n", + " const label = document.createElement('label');\n", + " label.textContent = `Downloading \"${filename}\": `;\n", + " div.appendChild(label);\n", + " const progress = document.createElement('progress');\n", + " progress.max = size;\n", + " div.appendChild(progress);\n", + " document.body.appendChild(div);\n", + "\n", + " const buffers = [];\n", + " let downloaded = 0;\n", + "\n", + " const channel = await google.colab.kernel.comms.open(id);\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + "\n", + " for await (const message of channel.messages) {\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + " if (message.buffers) {\n", + " for (const buffer of message.buffers) {\n", + " buffers.push(buffer);\n", + " downloaded += buffer.byteLength;\n", + " progress.value = downloaded;\n", + " }\n", + " }\n", + " }\n", + " const blob = new Blob(buffers, {type: 'application/binary'});\n", + " const a = document.createElement('a');\n", + " a.href = window.URL.createObjectURL(blob);\n", + " a.download = filename;\n", + " div.appendChild(a);\n", + " a.click();\n", + " div.remove();\n", + " }\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "download(\"download_f916373f-8b6d-46f5-b457-bbe87c562717\", \"Relat\\u00f3rio de Doa\\u00e7\\u00e3o (Rio Grande do Norte) - 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AnoPotencial DoadorPotencial Doador (PMP)Doador EfetivoDoador Efetivo (PMP)Percentual de EfetivaçãoEntrevista FamiliarNegativa FamiliarNegativa Familiar (%)
2201317755,94413,924,9%27914652,3%
3201417555,24413,925,1%23310545,1%
4201515746,13610,622,9%1447954,9%
5201615244,23911,325,7%975354,6%
6201717349,84713,527,2%1246653,2%
7201815945,3329,120,1%1017170,3%
8201921762,45214,924,0%1267156,3%
9202018853,6246,812,8%1006464,0%
10202120758,6195,49,2%1076762,6%
11202223164,93610,115,6%1318867,2%
12202322367,5329,714,3%1348966,4%
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AnoCoraçãoFígadoPulmãoRimPâncreasPâncreas RimIntestinoMultivisceralSubTotalCórneaTotal
count11.00000011.00000011.00000011.011.00000011.011.011.011.011.00000011.00000011.000000
mean2018.0000000.4545450.4545450.0231.0909090.00.00.00.0232.000000272.909091504.909091
std3.3166250.9341991.5075570.089.2395140.00.00.00.088.744577195.478109273.625457
min2013.0000000.0000000.0000000.061.0000000.00.00.00.066.00000055.000000139.000000
25%2015.5000000.0000000.0000000.0163.5000000.00.00.00.0163.500000111.000000278.500000
50%2018.0000000.0000000.0000000.0278.0000000.00.00.00.0278.000000201.000000500.000000
75%2020.5000000.5000000.0000000.0303.5000000.00.00.00.0304.000000428.500000718.500000
max2023.0000003.0000005.0000000.0330.0000000.00.00.00.0331.000000584.000000915.000000
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\"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Pulm\\u00e3o\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.8890872965260113,\n \"min\": 0.0,\n \"max\": 11.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.0,\n 11.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Rim\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 120.1813378396474,\n \"min\": 11.0,\n \"max\": 330.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 231.0909090909091,\n 278.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"P\\u00e2ncreas\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.8890872965260113,\n \"min\": 0.0,\n \"max\": 11.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.0,\n 11.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"P\\u00e2ncreas Rim\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.8890872965260113,\n \"min\": 0.0,\n \"max\": 11.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.0,\n 11.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Intestino\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.8890872965260113,\n \"min\": 0.0,\n \"max\": 11.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.0,\n 11.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Multivisceral\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.8890872965260113,\n \"min\": 0.0,\n \"max\": 11.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.0,\n 11.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SubTotal\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 119.81844348132738,\n \"min\": 11.0,\n \"max\": 331.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 232.0,\n 278.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"C\\u00f3rnea\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 193.20404102787063,\n \"min\": 11.0,\n \"max\": 584.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 272.90909090909093,\n 201.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Total\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 301.2780389983992,\n \"min\": 11.0,\n \"max\": 915.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 504.90909090909093,\n 500.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#salvar dataset\n", + "df.to_csv('dataset.csv', index=False)\n" + ], + "metadata": { + "id": "7gEPE87-aBGc" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#fazer dowload\n", + "from google.colab import files\n", + "files.download('dataset.csv')" + ], + "metadata": { + "id": "MgWXsfDEaZn-", + "outputId": "245feceb-d600-42ca-af7a-9860fb0a83e3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17 + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "\n", + " async function download(id, filename, size) {\n", + " if (!google.colab.kernel.accessAllowed) {\n", + " return;\n", + " }\n", + " const div = document.createElement('div');\n", + " const label = document.createElement('label');\n", + " label.textContent = `Downloading \"${filename}\": `;\n", + " div.appendChild(label);\n", + " const progress = document.createElement('progress');\n", + " progress.max = size;\n", + " div.appendChild(progress);\n", + " document.body.appendChild(div);\n", + "\n", + " const buffers = [];\n", + " let downloaded = 0;\n", + "\n", + " const channel = await google.colab.kernel.comms.open(id);\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + "\n", + " for await (const message of channel.messages) {\n", + " // Send a message to notify the kernel that we're ready.\n", + " channel.send({})\n", + " if (message.buffers) {\n", + " for (const buffer of message.buffers) {\n", + " buffers.push(buffer);\n", + " downloaded += buffer.byteLength;\n", + " progress.value = downloaded;\n", + " }\n", + " }\n", + " }\n", + " const blob = new Blob(buffers, {type: 'application/binary'});\n", + " const a = document.createElement('a');\n", + " a.href = window.URL.createObjectURL(blob);\n", + " a.download = filename;\n", + " div.appendChild(a);\n", + " a.click();\n", + " div.remove();\n", + " }\n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "application/javascript": [ + "download(\"download_ca4f65ad-e133-4659-9467-9807a3014b74\", \"dataset.csv\", 485)" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "df=pd.read_csv('/content/dataset.csv')\n", + "df.head(10)" + ], + "metadata": { + "id": "wpiupbNOWtlh" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#grafico ano\n" + ], + "metadata": { + "id": "8bDdilS4XU33" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/README.md b/README.md index 6f06bd0..8a9dbf3 100644 --- a/README.md +++ b/README.md @@ -1,104 +1,104 @@ -

- 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: -

- - +

+ 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: +

+ + diff --git "a/Relat\303\263rio de Doa\303\247\303\243o (Rio Grande do Norte) - Evolu\303\247\303\243o 2001 - 2023 - Table 1 (2).csv" "b/Relat\303\263rio de Doa\303\247\303\243o (Rio Grande do Norte) - Evolu\303\247\303\243o 2001 - 2023 - Table 1 (2).csv" new file mode 100644 index 0000000..38171ee --- /dev/null +++ "b/Relat\303\263rio de Doa\303\247\303\243o (Rio Grande do Norte) - Evolu\303\247\303\243o 2001 - 2023 - Table 1 (2).csv" @@ -0,0 +1,12 @@ +Ano,Potencial Doador,Potencial Doador (PMP),Doador Efetivo,Doador Efetivo (PMP),Percentual de Efetivação,Entrevista Familiar,Negativa Familiar,Negativa Familiar (%) +2013,177,"55,9",44,"13,9","24,9%",279,146,"52,3%" +2014,175,"55,2",44,"13,9","25,1%",233,105,"45,1%" +2015,157,"46,1",36,"10,6","22,9%",144,79,"54,9%" +2016,152,"44,2",39,"11,3","25,7%",97,53,"54,6%" +2017,173,"49,8",47,"13,5","27,2%",124,66,"53,2%" +2018,159,"45,3",32,"9,1","20,1%",101,71,"70,3%" +2019,217,"62,4",52,"14,9","24,0%",126,71,"56,3%" +2020,188,"53,6",24,"6,8","12,8%",100,64,"64,0%" +2021,207,"58,6",19,"5,4","9,2%",107,67,"62,6%" +2022,231,"64,9",36,"10,1","15,6%",131,88,"67,2%" +2023,223,"67,5",32,"9,7","14,3%",134,89,"66,4%" diff --git "a/Relat\303\263rio de Doa\303\247\303\243o (Rio Grande do Norte) - Evolu\303\247\303\243o 2001 - 2023.pdf" "b/Relat\303\263rio de Doa\303\247\303\243o (Rio Grande do Norte) - Evolu\303\247\303\243o 2001 - 2023.pdf" new file mode 100644 index 0000000..82e4d37 Binary files /dev/null and "b/Relat\303\263rio de Doa\303\247\303\243o (Rio Grande do Norte) - Evolu\303\247\303\243o 2001 - 2023.pdf" differ diff --git "a/Relat\303\263rio de Transplantes Realizados (Rio Grande do Norte) - Evolu\303\247\303\243o 2001 - 2023 - Table 1 (1).csv" "b/Relat\303\263rio de Transplantes Realizados (Rio Grande do Norte) - Evolu\303\247\303\243o 2001 - 2023 - Table 1 (1).csv" new file mode 100644 index 0000000..f4479c0 --- /dev/null +++ "b/Relat\303\263rio de Transplantes Realizados (Rio Grande do Norte) - Evolu\303\247\303\243o 2001 - 2023 - Table 1 (1).csv" @@ -0,0 +1,12 @@ +Ano,Coração,Fígado,Figado vivo,Figado falecido,Pancreas,Pulmão,Pulmão vivo,Pulmão falecido,Rim,Rim vivo,Rim falecido,Pancreas rim,Intestino Isolado,Multivisceral,Total Orgãos,Córnea,Médula óssea,MO Autólogo,Mo Aparentado,Mo Nap,Total Geral +2013,0,3,0,3,0,0,0,0,60,5,55,0,0,0,63,181,50,21,20,9,294 +2014,2,2,0,2,0,0,0,0,66,7,59,0,0,0,70,141,60,31,17,12,271 +2015,0,0,0,0,0,0,0,0,67,7,60,0,0,0,67,131,39,20,10,9,237 +2016,0,0,0,0,0,0,0,0,67,11,56,0,0,0,67,104,57,24,17,16,228 +2017,0,0,0,0,0,0,0,0,60,12,48,0,0,0,60,177,1,1,0,0,238 +2018,0,0,0,0,0,0,0,0,45,6,39,0,0,0,45,166,59,42,9,8,270 +2019,0,0,0,0,0,0,0,0,76,9,67,0,0,0,76,140,71,49,14,8,287 +2020,0,0,0,0,0,0,0,0,48,7,41,0,0,0,48,92,91,63,21,7,231 +2021,0,0,0,0,0,0,0,0,18,3,15,0,0,0,18,97,71,40,23,8,186 +2022,2,0,0,0,0,0,0,0,48,9,39,0,0,0,50,135,52,27,15,10,237 +2023,4,0,0,0,0,0,0,0,53,7,46,0,0,0,57,159,87,62,18,7,303 diff --git "a/Relat\303\263rio de Transplantes Realizados (Rio Grande do Norte) - Evolu\303\247\303\243o 2001 - 2023.pdf" "b/Relat\303\263rio de Transplantes Realizados (Rio Grande do Norte) - Evolu\303\247\303\243o 2001 - 2023.pdf" new file mode 100644 index 0000000..5a805b2 Binary files /dev/null and "b/Relat\303\263rio de Transplantes Realizados (Rio Grande do Norte) - Evolu\303\247\303\243o 2001 - 2023.pdf" differ diff --git "a/Relat\303\263rio de lista de espera por um transplante de \303\263rg\303\243o ou c\303\263rnea (Rio Grande do Norte) - S\303\251rie hist\303\263rica 2008-2023.pdf" "b/Relat\303\263rio de lista de espera por um transplante de \303\263rg\303\243o ou c\303\263rnea (Rio Grande do Norte) - S\303\251rie hist\303\263rica 2008-2023.pdf" new file mode 100644 index 0000000..2c386a6 Binary files /dev/null and "b/Relat\303\263rio de lista de espera por um transplante de \303\263rg\303\243o ou c\303\263rnea (Rio Grande do Norte) - S\303\251rie hist\303\263rica 2008-2023.pdf" differ diff --git a/S7.SAYONARA_TEMP b/S7.SAYONARA_TEMP new file mode 160000 index 0000000..9e5aa8c --- /dev/null +++ b/S7.SAYONARA_TEMP @@ -0,0 +1 @@ +Subproject commit 9e5aa8c916f8dc17b7c2db8c19d76a3c540e5fe5 diff --git a/dados.csv b/dados.csv new file mode 100644 index 0000000..805921d --- /dev/null +++ b/dados.csv @@ -0,0 +1,12 @@ +Ano,Potencial Doador,Potencial Doador (PMP),Doador Efetivo,Doador Efetivo (PMP),Percentual de Efetivação,Entrevista Familiar,Negativa Familiar,Negativa Familiar (%),Coração_x,Fígado_x,Figado vivo,Figado falecido,Pancreas,Pulmão_x,Pulmão vivo,Pulmão falecido,Rim_x,Rim vivo,Rim falecido,Pancreas rim,Intestino Isolado,Multivisceral_x,Total Orgãos,Córnea_x,Médula óssea,MO Autólogo,Mo Aparentado,Mo Nap,Total Geral,Coração_y,Fígado_y,Pulmão_y,Rim_y,Pâncreas,Pâncreas Rim,Intestino,Multivisceral_y,SubTotal,Córnea_y,Total +2013,177,"55,9",44,"13,9","24,9%",279,146,"52,3%",0,3,0,3,0,0,0,0,60,5,55,0,0,0,63,181,50,21,20,9,294,0,5,0,61,0,0,0,0,66,73,139 +2014,175,"55,2",44,"13,9","25,1%",233,105,"45,1%",2,2,0,2,0,0,0,0,66,7,59,0,0,0,70,141,60,31,17,12,271,0,0,0,153,0,0,0,0,153,55,208 +2015,157,"46,1",36,"10,6","22,9%",144,79,"54,9%",0,0,0,0,0,0,0,0,67,7,60,0,0,0,67,131,39,20,10,9,237,0,0,0,180,0,0,0,0,180,66,246 +2016,152,"44,2",39,"11,3","25,7%",97,53,"54,6%",0,0,0,0,0,0,0,0,67,11,56,0,0,0,67,104,57,24,17,16,228,0,0,0,162,0,0,0,0,162,149,311 +2017,173,"49,8",47,"13,5","27,2%",124,66,"53,2%",0,0,0,0,0,0,0,0,60,12,48,0,0,0,60,177,1,1,0,0,238,0,0,0,165,0,0,0,0,165,180,345 +2018,159,"45,3",32,"9,1","20,1%",101,71,"70,3%",0,0,0,0,0,0,0,0,45,6,39,0,0,0,45,166,59,42,9,8,270,0,0,0,299,0,0,0,0,299,201,500 +2019,217,"62,4",52,"14,9","24,0%",126,71,"56,3%",0,0,0,0,0,0,0,0,76,9,67,0,0,0,76,140,71,49,14,8,287,0,0,0,306,0,0,0,0,306,296,602 +2020,188,"53,6",24,"6,8","12,8%",100,64,"64,0%",0,0,0,0,0,0,0,0,48,7,41,0,0,0,48,92,91,63,21,7,231,0,0,0,278,0,0,0,0,278,378,656 +2021,207,"58,6",19,"5,4","9,2%",107,67,"62,6%",0,0,0,0,0,0,0,0,18,3,15,0,0,0,18,97,71,40,23,8,186,1,0,0,301,0,0,0,0,302,479,781 +2022,231,"64,9",36,"10,1","15,6%",131,88,"67,2%",2,0,0,0,0,0,0,0,48,9,39,0,0,0,50,135,52,27,15,10,237,3,0,0,307,0,0,0,0,310,541,851 +2023,223,"67,5",32,"9,7","14,3%",134,89,"66,4%",4,0,0,0,0,0,0,0,53,7,46,0,0,0,57,159,87,62,18,7,303,1,0,0,330,0,0,0,0,331,584,915 diff --git a/dataset.csv b/dataset.csv new file mode 100644 index 0000000..1737ff5 --- /dev/null +++ b/dataset.csv @@ -0,0 +1,12 @@ +Ano,Coração,Fígado,Pulmão,Rim,Pâncreas,Pâncreas Rim,Intestino,Multivisceral,SubTotal,Córnea,Total +2013,0,5,0,61,0,0,0,0,66,73,139 +2014,0,0,0,153,0,0,0,0,153,55,208 +2015,0,0,0,180,0,0,0,0,180,66,246 +2016,0,0,0,162,0,0,0,0,162,149,311 +2017,0,0,0,165,0,0,0,0,165,180,345 +2018,0,0,0,299,0,0,0,0,299,201,500 +2019,0,0,0,306,0,0,0,0,306,296,602 +2020,0,0,0,278,0,0,0,0,278,378,656 +2021,1,0,0,301,0,0,0,0,302,479,781 +2022,3,0,0,307,0,0,0,0,310,541,851 +2023,1,0,0,330,0,0,0,0,331,584,915 diff --git a/material/analise-exploratoria/analise.ipynb b/material/analise-exploratoria/analise.ipynb index 1cce302..b491a17 100644 --- a/material/analise-exploratoria/analise.ipynb +++ b/material/analise-exploratoria/analise.ipynb @@ -1,22 +1,22 @@ -{ - "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 -} +{ + "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/nome-projeto.md b/material/nome-projeto.md index 55c478b..c755f7a 100644 --- a/material/nome-projeto.md +++ b/material/nome-projeto.md @@ -1,12 +1,12 @@ -## 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) - +## 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