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o Snowpark?\u003C/h3\u003E\n","\u003Cp\u003ETrata-se do conjunto de bibliotecas e sistemas de runtime (tempo de execução) do Snowflake que implementam e processam códigos não SQL de forma segura, incluindo Python, Java e Scala.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EBibliotecas conhecidas dos clientes\u003C/strong\u003E: o Snowpark oferece interfaces de programação de aplicativos (application programming interface, APIs) totalmente integradas, com programação no estilo DataFrame e compatíveis com sistemas de suporte operacional (operational support system, OSS) nas linguagens que os operadores de dados gostam de usar. Ele também conta com a API Snowpark ML, para uma modelagem de aprendizado de máquina (machine learning, ML) (em versão preliminar pública) e operações de ML (em versão preliminar privada) mais eficientes.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EEstrutura de runtime flexível\u003C/strong\u003E: o Snowpark oferece estruturas de runtime flexíveis que permitem aos usuários inserir e executar uma lógica personalizada. Os desenvolvedores podem criar pipelines de dados, modelos de ML e aplicações de dados com facilidade, utilizando funções definidas pelo usuário e procedimentos armazenados.\u003C/p\u003E\n","\u003Cp\u003ESaiba mais sobre o \u003Ca href=\"/snowpark/\"\u003ESnowpark\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpark-in-snowflake-python-worksheets-ptbr/snowpark.png\" alt=\"Snowpark\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EO que são as planilhas Python?\u003C/h3\u003E\n","\u003Cp\u003EAs planilhas Python são um novo tipo de planilha no Snowsight que ajudam você a começar a usar o Snowpark com mais rapidez. Os usuários podem desenvolver pipelines de dados, aplicações e modelos de ML diretamente no Snowflake, sem precisar de um ambiente de desenvolvimento integrado (integrated development environment, IDE) adicional para executar, configurar ou manter no Python. Essas planilhas também podem ser convertidas em procedimentos para agendamento das suas aplicações Snowpark.\u003C/p\u003E\n","\u003Ch3\u003EVocê vai aprender como\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECarregar dados de tabelas do Snowflake nos DataFrames do Snowpark.\u003C/li\u003E\u003Cli\u003EExecutar uma análise de dados exploratória nos DataFrames do Snowpark.\u003C/li\u003E\u003Cli\u003EDinamizar e combinar dados de várias tabelas usando os DataFrames do Snowpark.\u003C/li\u003E\u003Cli\u003ESalvar dados transformados em uma tabela do Snowflake.\u003C/li\u003E\u003Cli\u003EImplementar uma planilha Python como um procedimento armazenado.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EO que será desenvolvido\u003C/h3\u003E\n","\u003Cp\u003EUm conjunto de dados preparado que pode ser usado em análises e aplicações mais adiante no processo para, por exemplo, treinar um modelo de aprendizado de máquina.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EIntrodução\u003C/h2\u003E\n","\u003Ch3\u003ECriar uma conta de avaliação Snowflake\u003C/h3\u003E\n","\u003Cp\u003EClique no \u003Ca href=\"https://signup.snowflake.com/?lab=getStartedWithSnowparkInPythonWorksheets\"\u003Elink\u003C/a\u003E para obter uma conta de avaliação gratuita de 30 dias. Você acessará a página de configuração da avaliação intitulada \u003Cstrong\u003EIntrodução ao Snowpark em planilhas Python para Snowflake\u003C/strong\u003E. Recomendamos escolher a região mais próxima a você. Também recomendamos escolher a edição mais popular, Enterprise, mas qualquer uma funcionará para este laboratório.\u003C/p\u003E\n","\u003Ch3\u003ELogin e configuração do laboratório\u003C/h3\u003E\n","\u003Cp\u003EAcesse sua conta Snowflake. Para acessar este laboratório e configurar as amostras de dados e o código, clique no link a seguir.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIMPORTANTE: é preciso ter a função ACCOUNTADMIN para prosseguir com a configuração do laboratório descrita abaixo. Observação: caso tenha criado uma nova conta de avaliação, você terá a função ACCOUNTADMIN automaticamente.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E<button>\u003Ca href=\"https://app.snowflake.com/resources/labs/getStartedWithSnowparkInPythonWorksheets\"\u003EConfigurar o ambiente de laboratório\u003C/a\u003E</button>\u003C/p\u003E\n","\u003Cp\u003EO botão acima vai levar você até a página de avaliação intitulada \u003Cstrong\u003EIntrodução ao Snowpark em planilhas Python para Snowflake\u003C/strong\u003E. Na página, você precisará clicar em \u003Cstrong\u003ESetup Lab\u003C/strong\u003E. O processo leva menos de um minuto e oferece uma configuração com amostras de dados e código Python com explicações.\u003C/p\u003E\n","\u003Cp\u003E==================================================================================================\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpark-in-snowflake-python-worksheets-ptbr/setup_lab.png\" alt=\"Implementação\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ECarregar dados de tabelas do Snowflake nos DataFrames do Snowpark\u003C/h2\u003E\n","\u003Cp\u003EPrimeiro, vamos importar a biblioteca Snowpark Python.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Import Snowpark for Python\nimport snowflake.snowpark as snowpark \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECarregar dados agregados de despesas e receitas da campanha\u003C/h3\u003E\n","\u003Cp\u003EA tabela campaign spend contém dados de cliques em anúncios que foram agregados para mostrar as despesas diárias em diferentes canais de anúncios digitais, incluindo mecanismos de busca, redes sociais, emails e vídeos. A tabela revenue contém dados de receita de 10 anos.\u003C/p\u003E\n","\u003Cp\u003ENeste exemplo, usaremos o código a seguir para carregar os dados das tabelas \u003Cem\u003E\u003Cstrong\u003Ecampaign_spend\u003C/strong\u003E\u003C/em\u003E e \u003Cem\u003E\u003Cstrong\u003Emonthly_revenue\u003C/strong\u003E\u003C/em\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esnow_df_spend = session.table('campaign_spend')\nsnow_df_revenue = session.table('monthly_revenue')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA seguir, veja outras formas de carregar dados nos DataFrames do Snowpark.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003Esession.sql("select col1, col2... from tableName")\u003C/li\u003E\u003Cli\u003Esession.read.options({"field_delimiter": ",", "skip_header": 1}).schema(user_schema).csv("@mystage/testCSV.csv")\u003C/li\u003E\u003Cli\u003Esession.read.parquet("@stageName/path/to/file")\u003C/li\u003E\u003Cli\u003Esession.create_dataframe([1,2,3], schema=["col1"])\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EDICA: saiba mais sobre os \u003Ca href=\"https://docs.snowflake.com/pt/developer-guide/snowpark/reference/python/latest/dataframe\"\u003EDataFrames do Snowpark\u003C/a\u003E.\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ETransformações de dados\u003C/h2\u003E\n","\u003Cp\u003ENesta seção, vamos executar uma série de transformações, incluindo agregações e combinação de dois dataframes.\u003C/p\u003E\n","\u003Cp\u003EPrimeiro, vamos importar todas as funções necessárias.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.snowpark.functions import month,year,col,sum\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDespesa total por ano e por mês para todos os canais\u003C/h3\u003E\n","\u003Cp\u003EVamos transformar os dados para que possamos ver o \u003Cstrong\u003Ecusto total por ano/mês por canal\u003C/strong\u003E usando as funções \u003Cem\u003E\u003Cstrong\u003Egroup_by()\u003C/strong\u003E\u003C/em\u003E e \u003Cem\u003E\u003Cstrong\u003Eagg()\u003C/strong\u003E\u003C/em\u003E do DataFrame do Snowpark.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esnow_df_spend_per_channel = snow_df_spend.group_by(year('DATE'), month('DATE'),'CHANNEL').agg(sum('TOTAL_COST').as_('TOTAL_COST')).with_column_renamed('"YEAR(DATE)"',"YEAR").with_column_renamed('"MONTH(DATE)"',"MONTH").sort('YEAR','MONTH')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDICA: veja a lista completa de \u003Ca href=\"https://docs.snowflake.com/pt/developer-guide/snowpark/reference/python/latest/functions\"\u003Efunções\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EPara visualizar o resultado dessa transformação, podemos usar a função \u003Cem\u003E\u003Cstrong\u003Eshow()\u003C/strong\u003E\u003C/em\u003E no DataFrame do Snowpark, que apresentará o resultado na guia \u003Cstrong\u003EOutput\u003C/strong\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eprint("Total Spend per Year and Month For All Channels")\nsnow_df_spend_per_channel.show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDespesa total em todos os canais\u003C/h3\u003E\n","\u003Cp\u003EVamos transformar ainda mais os dados das despesas da campanha para que cada linha represente o custo total em todos os canais por ano/mês usando as funções \u003Cem\u003E\u003Cstrong\u003Epivot()\u003C/strong\u003E\u003C/em\u003E e \u003Cem\u003E\u003Cstrong\u003Esum()\u003C/strong\u003E\u003C/em\u003E no DataFrame do Snowpark.\u003C/p\u003E\n","\u003Cp\u003EEssa transformação vai permitir combinar a tabela de receita para que tenhamos os recursos de entrada e a variável-alvo em uma única tabela para treinamento de modelos.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esnow_df_spend_per_month = snow_df_spend_per_channel.pivot('CHANNEL',['search_engine','social_media','video','email']).sum('TOTAL_COST').sort('YEAR','MONTH')\nsnow_df_spend_per_month = snow_df_spend_per_month.select(\n    col("YEAR"),\n    col("MONTH"),\n    col("'search_engine'").as_("SEARCH_ENGINE"),\n    col("'social_media'").as_("SOCIAL_MEDIA"),\n    col("'video'").as_("VIDEO"),\n    col("'email'").as_("EMAIL")\n)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EPara visualizar o resultado dessa transformação, podemos usar a função \u003Cem\u003E\u003Cstrong\u003Eshow()\u003C/strong\u003E\u003C/em\u003E no DataFrame do Snowpark, que apresentará o resultado na guia \u003Cstrong\u003EOutput\u003C/strong\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eprint("Total Spend Across All Channels")\nsnow_df_spend_per_month.show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDados de receita total por ano e por mês\u003C/h3\u003E\n","\u003Cp\u003EAgora vamos transformar os dados de receita em receita por ano/mês usando as funções \u003Cem\u003E\u003Cstrong\u003Egroup_by()\u003C/strong\u003E\u003C/em\u003E e ***agg()***.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esnow_df_revenue_per_month = snow_df_revenue.group_by('YEAR','MONTH').agg(sum('REVENUE')).sort('YEAR','MONTH').with_column_renamed('SUM(REVENUE)','REVENUE')\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EPara visualizar o resultado dessa transformação, podemos usar a função \u003Cem\u003E\u003Cstrong\u003Eshow()\u003C/strong\u003E\u003C/em\u003E no DataFrame do Snowpark, que apresentará o resultado na guia \u003Cstrong\u003EOutput\u003C/strong\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eprint("Total Revenue per Year and Month")\nsnow_df_revenue_per_month.show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECombinação de despesas e receitas totais por ano e por mês em todos os canais\u003C/h3\u003E\n","\u003Cp\u003EA seguir, vamos combinar os dados de receita com os dados de despesas da campanha transformados, para que nossos recursos de entrada (ou seja, o custo por canal) e a variável-alvo (ou seja, a receita) possam ser carregados em uma única tabela para treinamento de modelos e análises.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esnow_df_spend_and_revenue_per_month = snow_df_spend_per_month.join(snow_df_revenue_per_month, ["YEAR","MONTH"])\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EPara visualizar o resultado dessa transformação, podemos usar a função \u003Cem\u003E\u003Cstrong\u003Eshow()\u003C/strong\u003E\u003C/em\u003E no DataFrame do Snowpark, que apresentará o resultado na guia \u003Cstrong\u003EOutput\u003C/strong\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eprint("Total Spend and Revenue per Year and Month Across All Channels")\nsnow_df_spend_and_revenue_per_month.show()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EVerificação do plano de consultas com Explain\u003C/h3\u003E\n","\u003Cp\u003EO Snowpark facilita a observação do plano de consulta e execução do DataFrame usando a função \u003Cem\u003E\u003Cstrong\u003Eexplain()\u003C/strong\u003E\u003C/em\u003E do DataFrame do Snowpark.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esnow_df_spend_and_revenue_per_month.explain()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EO resultado da instrução acima pode ser visualizado na guia \u003Cstrong\u003EOutput\u003C/strong\u003E.\u003C/p\u003E\n","\u003Ch3\u003EGuia Output\u003C/h3\u003E\n","\u003Cp\u003EAqui demonstramos o resultado da guia \u003Cstrong\u003EOutput\u003C/strong\u003E após executar a planilha.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpark-in-snowflake-python-worksheets-ptbr/run_python_worksheet.gif\" alt=\"Planilha\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003ESalvar os dados transformados\u003C/h2\u003E\n","\u003Cp\u003EVamos salvar os dados transformados em uma tabela Snowflake chamada \u003Cem\u003E\u003Cstrong\u003ESPEND_AND_REVENUE_PER_MONTH\u003C/strong\u003E\u003C/em\u003E para que eles possam ser usados para análise e/ou treinamento de modelos.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Esnow_df_spend_and_revenue_per_month.write.mode('overwrite').save_as_table('SPEND_AND_REVENUE_PER_MONTH')\n\u003C/code\u003E\u003C/pre\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EVisualização e retorno de dados transformados\u003C/h2\u003E\n","\u003Cp\u003EUm dos valores retornados de uma planilha Python é do tipo \u003Cem\u003E\u003Cstrong\u003ETable()\u003C/strong\u003E\u003C/em\u003E e, no nosso caso, isso permitirá que você exiba e retorne os dados transformados como um Dataframe do Snowpark.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ereturn snow_df_spend_and_revenue_per_month\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EO resultado da instrução acima pode ser visualizado na guia \u003Cstrong\u003EResults\u003C/strong\u003E conforme abaixo.\u003C/p\u003E\n","\u003Ch3\u003EGuia Results\u003C/h3\u003E\n","\u003Cp\u003EAqui demonstramos o resultado da guia \u003Cstrong\u003EResults\u003C/strong\u003E após executar a planilha.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpark-in-snowflake-python-worksheets-ptbr/run_python_worksheet.gif\" alt=\"Planilha\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EImplementação como um procedimento armazenado\u003C/h2\u003E\n","\u003Cp\u003EComo alternativa, também é possível implementar essa planilha como um procedimento armazenado Python que você pode agendar usando o \u003Ca href=\"https://docs.snowflake.com/pt/user-guide/tasks-intro\"\u003ESnowflake Tasks\u003C/a\u003E, por exemplo. Para implementar essa planilha, clique em \u003Cstrong\u003EDeploy\u003C/strong\u003E no canto superior direito e siga as instruções abaixo.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpark-in-snowflake-python-worksheets-ptbr/deploy1.png\" alt=\"Implementação\"\u003E\u003C/p\u003E\n","\u003Cp\u003E==================================================================================================\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpark-in-snowflake-python-worksheets-ptbr/deploy2.png\" alt=\"Implementação\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EVisualização do procedimento armazenado\u003C/h3\u003E\n","\u003Cp\u003EApós ser implementado, os detalhes do procedimento armazenado podem ser visualizados em \u003Cstrong\u003EData >> Databases >> SNOWPARK_DEMO_SCHEMA >> Procedures\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpark-in-snowflake-python-worksheets-ptbr/deploy3.png\" alt=\"Implementação\"\u003E\u003C/p\u003E\n<!-- ------------------------ -->\n","\u003Ch2\u003EConclusão e recursos\u003C/h2\u003E\n","\u003Cp\u003EParabéns! Você realizou com sucesso as tarefas de engenharia de dados usando o Snowpark nas planilhas Python para Snowflake.\u003C/p\u003E\n","\u003Cp\u003EAdoraríamos saber sua opinião sobre este quickstart guide! Preencha este \u003Ca href=\"https://docs.google.com/forms/d/e/1FAIpQLScpCO4ekMB9kitQ6stQZ1NLqZf3VqbQfDrf7yRIwMQjty57_g/viewform?usp=sf_link\"\u003Eformulário de feedback\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EVocê aprendeu a\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECarregar dados de tabelas do Snowflake nos DataFrames do Snowpark.\u003C/li\u003E\u003Cli\u003EExecutar uma análise de dados exploratória nos DataFrames do Snowpark.\u003C/li\u003E\u003Cli\u003EDinamizar e combinar dados de várias tabelas usando os DataFrames do Snowpark.\u003C/li\u003E\u003Cli\u003ESalvar dados transformados em uma tabela do Snowflake.\u003C/li\u003E\u003Cli\u003EImplementar uma planilha Python como um procedimento armazenado.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPróximas etapas\u003C/h3\u003E\n","\u003Cp\u003EPara aprender a processar dados de maneira incremental, orquestrar pipelines de dados com o Snowflake Tasks, implementá-los via pipelines de integração e entrega contínuas (continuous integration and continuous delivery, CI/CD), bem como usar a nova ferramenta de interface de linha de comando (command-line interface, CLI) do desenvolvedor Snowflake, além da extensão Visual Studio Code, continue a sua jornada e aprofunde os seus conhecimentos no quickstart \u003Ca href=\"/en/developers/guides/data-engineering-pipelines-with-snowpark-python/\"\u003EPipelines de engenharia de dados com Snowpark Python\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003ERecursos relacionados\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/pt/developer-guide/snowpark/python/python-worksheets\"\u003EGuia de desenvolvedor de planilhas Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/guide/getting_started_with_dataengineering_ml_using_snowpark_python_ptbr/index.html\"\u003EIntrodução à engenharia de dados e ML com Snowpark para Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/en/developers/guides/getting-started-snowpark-machine-learning/\"\u003EAvançado: guia de aprendizado de máquina com Snowpark para Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/Snowflake-Labs/snowpark-python-demos/blob/main/README.md\"\u003EDemonstrações do Snowpark para Python\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/pt/developer-guide/snowpark/python/index\"\u003EGuia do desenvolvedor de Snowpark para Python\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Introdução ao Snowpark em planilhas Python para Snowflake","isDeveloperGuidesPage":false,"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"\u003C!-- ------------------------ --\u003E\r\n## Visão geral\r\n\r\n\r\nAo concluir este guia, você poderá testar o Snowpark para Python dentro da interface do Snowflake, e vai ter uma compreensão melhor de como realizar tarefas essenciais de engenharia de dados usando o Snowpark em uma planilha Snowflake Python.\r\n\r\n### O que é o Snowpark?\r\n\r\nTrata-se do conjunto de bibliotecas e sistemas de runtime (tempo de execução) do Snowflake que implementam e processam códigos não SQL de forma segura, incluindo Python, Java e Scala.\r\n\r\n**Bibliotecas conhecidas dos clientes**: o Snowpark oferece interfaces de programação de aplicativos (application programming interface, APIs) totalmente integradas, com programação no estilo DataFrame e compatíveis com sistemas de suporte operacional (operational support system, OSS) nas linguagens que os operadores de dados gostam de usar. Ele também conta com a API Snowpark ML, para uma modelagem de aprendizado de máquina (machine learning, ML) (em versão preliminar pública) e operações de ML (em versão preliminar privada) mais eficientes.\r\n\r\n**Estrutura de runtime flexível**: o Snowpark oferece estruturas de runtime flexíveis que permitem aos usuários inserir e executar uma lógica personalizada. Os desenvolvedores podem criar pipelines de dados, modelos de ML e aplicações de dados com facilidade, utilizando funções definidas pelo usuário e procedimentos armazenados.\r\n\r\nSaiba mais sobre o [Snowpark](/snowpark/).\r\n\r\n![Snowpark](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpark-in-snowflake-python-worksheets-ptbr/snowpark.png)\r\n\r\n### O que são as planilhas Python?\r\n\r\nAs planilhas Python são um novo tipo de planilha no Snowsight que ajudam você a começar a usar o Snowpark com mais rapidez. Os usuários podem desenvolver pipelines de dados, aplicações e modelos de ML diretamente no Snowflake, sem precisar de um ambiente de desenvolvimento integrado (integrated development environment, IDE) adicional para executar, configurar ou manter no Python. Essas planilhas também podem ser convertidas em procedimentos para agendamento das suas aplicações Snowpark.\r\n\r\n### Você vai aprender como\r\n\r\n- Carregar dados de tabelas do Snowflake nos DataFrames do Snowpark.\r\n- Executar uma análise de dados exploratória nos DataFrames do Snowpark.\r\n- Dinamizar e combinar dados de várias tabelas usando os DataFrames do Snowpark.\r\n- Salvar dados transformados em uma tabela do Snowflake.\r\n- Implementar uma planilha Python como um procedimento armazenado.\r\n\r\n### O que será desenvolvido\r\n\r\nUm conjunto de dados preparado que pode ser usado em análises e aplicações mais adiante no processo para, por exemplo, treinar um modelo de aprendizado de máquina.\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Introdução\r\n\r\n\r\n### Criar uma conta de avaliação Snowflake\r\n\r\nClique no [link](https://signup.snowflake.com/?lab=getStartedWithSnowparkInPythonWorksheets) para obter uma conta de avaliação gratuita de 30 dias. Você acessará a página de configuração da avaliação intitulada **Introdução ao Snowpark em planilhas Python para Snowflake**. Recomendamos escolher a região mais próxima a você. Também recomendamos escolher a edição mais popular, Enterprise, mas qualquer uma funcionará para este laboratório.\r\n\r\n### Login e configuração do laboratório\r\n\r\nAcesse sua conta Snowflake. Para acessar este laboratório e configurar as amostras de dados e o código, clique no link a seguir.\r\n\r\n\u003E IMPORTANTE: é preciso ter a função ACCOUNTADMIN para prosseguir com a configuração do laboratório descrita abaixo. Observação: caso tenha criado uma nova conta de avaliação, você terá a função ACCOUNTADMIN automaticamente.\r\n\r\n\u003Cbutton\u003E[Configurar o ambiente de laboratório](https://app.snowflake.com/resources/labs/getStartedWithSnowparkInPythonWorksheets)\u003C/button\u003E\r\n\r\nO botão acima vai levar você até a página de avaliação intitulada **Introdução ao Snowpark em planilhas Python para Snowflake**. Na página, você precisará clicar em **Setup Lab**. O processo leva menos de um minuto e oferece uma configuração com amostras de dados e código Python com explicações.\r\n\r\n==================================================================================================\r\n\r\n![Implementação](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpark-in-snowflake-python-worksheets-ptbr/setup_lab.png)\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Carregar dados de tabelas do Snowflake nos DataFrames do Snowpark\r\n\r\n\r\nPrimeiro, vamos importar a biblioteca Snowpark Python.\r\n\r\n```python\r\n# Import Snowpark for Python\r\nimport snowflake.snowpark as snowpark \r\n```\r\n\r\n### Carregar dados agregados de despesas e receitas da campanha\r\n\r\n\r\nA tabela campaign spend contém dados de cliques em anúncios que foram agregados para mostrar as despesas diárias em diferentes canais de anúncios digitais, incluindo mecanismos de busca, redes sociais, emails e vídeos. A tabela revenue contém dados de receita de 10 anos.\r\n\r\nNeste exemplo, usaremos o código a seguir para carregar os dados das tabelas ***campaign_spend*** e ***monthly_revenue***.\r\n\r\n```python\r\nsnow_df_spend = session.table('campaign_spend')\r\nsnow_df_revenue = session.table('monthly_revenue')\r\n```\r\n\r\nA seguir, veja outras formas de carregar dados nos DataFrames do Snowpark.\r\n\r\n- session.sql(\"select col1, col2... from tableName\")\r\n- session.read.options({\"field_delimiter\": \",\", \"skip_header\": 1}).schema(user_schema).csv(\"@mystage/testCSV.csv\")\r\n- session.read.parquet(\"@stageName/path/to/file\")\r\n- session.create_dataframe([1,2,3], schema=[\"col1\"])\r\n\r\nDICA: saiba mais sobre os [DataFrames do Snowpark](https://docs.snowflake.com/pt/developer-guide/snowpark/reference/python/latest/dataframe).\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Transformações de dados\r\n\r\n\r\nNesta seção, vamos executar uma série de transformações, incluindo agregações e combinação de dois dataframes.\r\n\r\nPrimeiro, vamos importar todas as funções necessárias.\r\n\r\n```python\r\nfrom snowflake.snowpark.functions import month,year,col,sum\r\n```\r\n\r\n### Despesa total por ano e por mês para todos os canais\r\n\r\nVamos transformar os dados para que possamos ver o **custo total por ano/mês por canal** usando as funções ***group_by()*** e ***agg()*** do DataFrame do Snowpark.\r\n\r\n```python\r\nsnow_df_spend_per_channel = snow_df_spend.group_by(year('DATE'), month('DATE'),'CHANNEL').agg(sum('TOTAL_COST').as_('TOTAL_COST')).with_column_renamed('\"YEAR(DATE)\"',\"YEAR\").with_column_renamed('\"MONTH(DATE)\"',\"MONTH\").sort('YEAR','MONTH')\r\n```\r\n\r\nDICA: veja a lista completa de [funções](https://docs.snowflake.com/pt/developer-guide/snowpark/reference/python/latest/functions).\r\n\r\nPara visualizar o resultado dessa transformação, podemos usar a função ***show()*** no DataFrame do Snowpark, que apresentará o resultado na guia **Output**.\r\n\r\n```python\r\nprint(\"Total Spend per Year and Month For All Channels\")\r\nsnow_df_spend_per_channel.show()\r\n```\r\n\r\n### Despesa total em todos os canais\r\n\r\nVamos transformar ainda mais os dados das despesas da campanha para que cada linha represente o custo total em todos os canais por ano/mês usando as funções ***pivot()*** e ***sum()*** no DataFrame do Snowpark.\r\n\r\nEssa transformação vai permitir combinar a tabela de receita para que tenhamos os recursos de entrada e a variável-alvo em uma única tabela para treinamento de modelos.\r\n\r\n```python\r\nsnow_df_spend_per_month = snow_df_spend_per_channel.pivot('CHANNEL',['search_engine','social_media','video','email']).sum('TOTAL_COST').sort('YEAR','MONTH')\r\nsnow_df_spend_per_month = snow_df_spend_per_month.select(\r\n    col(\"YEAR\"),\r\n    col(\"MONTH\"),\r\n    col(\"'search_engine'\").as_(\"SEARCH_ENGINE\"),\r\n    col(\"'social_media'\").as_(\"SOCIAL_MEDIA\"),\r\n    col(\"'video'\").as_(\"VIDEO\"),\r\n    col(\"'email'\").as_(\"EMAIL\")\r\n)\r\n```\r\n\r\nPara visualizar o resultado dessa transformação, podemos usar a função ***show()*** no DataFrame do Snowpark, que apresentará o resultado na guia **Output**.\r\n\r\n```python\r\nprint(\"Total Spend Across All Channels\")\r\nsnow_df_spend_per_month.show()\r\n```\r\n\r\n### Dados de receita total por ano e por mês\r\n\r\nAgora vamos transformar os dados de receita em receita por ano/mês usando as funções ***group_by()*** e ***agg()***.\r\n\r\n```python\r\nsnow_df_revenue_per_month = snow_df_revenue.group_by('YEAR','MONTH').agg(sum('REVENUE')).sort('YEAR','MONTH').with_column_renamed('SUM(REVENUE)','REVENUE')\r\n```\r\n\r\nPara visualizar o resultado dessa transformação, podemos usar a função ***show()*** no DataFrame do Snowpark, que apresentará o resultado na guia **Output**.\r\n\r\n```python\r\nprint(\"Total Revenue per Year and Month\")\r\nsnow_df_revenue_per_month.show()\r\n```\r\n\r\n### Combinação de despesas e receitas totais por ano e por mês em todos os canais\r\n\r\nA seguir, vamos combinar os dados de receita com os dados de despesas da campanha transformados, para que nossos recursos de entrada (ou seja, o custo por canal) e a variável-alvo (ou seja, a receita) possam ser carregados em uma única tabela para treinamento de modelos e análises.\r\n\r\n```python\r\nsnow_df_spend_and_revenue_per_month = snow_df_spend_per_month.join(snow_df_revenue_per_month, [\"YEAR\",\"MONTH\"])\r\n```\r\n\r\nPara visualizar o resultado dessa transformação, podemos usar a função ***show()*** no DataFrame do Snowpark, que apresentará o resultado na guia **Output**.\r\n\r\n```python\r\nprint(\"Total Spend and Revenue per Year and Month Across All Channels\")\r\nsnow_df_spend_and_revenue_per_month.show()\r\n```\r\n\r\n### Verificação do plano de consultas com Explain\r\n\r\nO Snowpark facilita a observação do plano de consulta e execução do DataFrame usando a função ***explain()*** do DataFrame do Snowpark.\r\n\r\n```python\r\nsnow_df_spend_and_revenue_per_month.explain()\r\n```\r\n\r\nO resultado da instrução acima pode ser visualizado na guia **Output**.\r\n\r\n### Guia Output\r\n\r\nAqui demonstramos o resultado da guia **Output** após executar a planilha.\r\n\r\n![Planilha](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpark-in-snowflake-python-worksheets-ptbr/run_python_worksheet.gif)\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Salvar os dados transformados\r\n\r\n\r\nVamos salvar os dados transformados em uma tabela Snowflake chamada ***SPEND_AND_REVENUE_PER_MONTH*** para que eles possam ser usados para análise e/ou treinamento de modelos.\r\n\r\n```python\r\nsnow_df_spend_and_revenue_per_month.write.mode('overwrite').save_as_table('SPEND_AND_REVENUE_PER_MONTH')\r\n```\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Visualização e retorno de dados transformados\r\n\r\n\r\nUm dos valores retornados de uma planilha Python é do tipo ***Table()*** e, no nosso caso, isso permitirá que você exiba e retorne os dados transformados como um Dataframe do Snowpark.\r\n\r\n```python\r\nreturn snow_df_spend_and_revenue_per_month\r\n```\r\n\r\nO resultado da instrução acima pode ser visualizado na guia **Results** conforme abaixo.\r\n\r\n### Guia Results\r\n\r\nAqui demonstramos o resultado da guia **Results** após executar a planilha.\r\n\r\n![Planilha](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpark-in-snowflake-python-worksheets-ptbr/run_python_worksheet.gif)\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Implementação como um procedimento armazenado\r\n\r\n\r\nComo alternativa, também é possível implementar essa planilha como um procedimento armazenado Python que você pode agendar usando o [Snowflake Tasks](https://docs.snowflake.com/pt/user-guide/tasks-intro), por exemplo. Para implementar essa planilha, clique em **Deploy** no canto superior direito e siga as instruções abaixo.\r\n\r\n![Implementação](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpark-in-snowflake-python-worksheets-ptbr/deploy1.png)\r\n\r\n==================================================================================================\r\n\r\n![Implementação](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpark-in-snowflake-python-worksheets-ptbr/deploy2.png)\r\n\r\n### Visualização do procedimento armazenado\r\n\r\nApós ser implementado, os detalhes do procedimento armazenado podem ser visualizados em **Data \u003E\u003E Databases \u003E\u003E SNOWPARK_DEMO_SCHEMA \u003E\u003E Procedures**.\r\n\r\n![Implementação](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/getting-started-with-snowpark-in-snowflake-python-worksheets-ptbr/deploy3.png)\r\n\r\n\u003C!-- ------------------------ --\u003E\r\n## Conclusão e recursos\r\n\r\nParabéns! Você realizou com sucesso as tarefas de engenharia de dados usando o Snowpark nas planilhas Python para Snowflake.\r\n\r\nAdoraríamos saber sua opinião sobre este quickstart guide! Preencha este [formulário de feedback](https://docs.google.com/forms/d/e/1FAIpQLScpCO4ekMB9kitQ6stQZ1NLqZf3VqbQfDrf7yRIwMQjty57_g/viewform?usp=sf_link).\r\n\r\n### Você aprendeu a\r\n\r\n- Carregar dados de tabelas do Snowflake nos DataFrames do Snowpark.\r\n- Executar uma análise de dados exploratória nos DataFrames do Snowpark.\r\n- Dinamizar e combinar dados de várias tabelas usando os DataFrames do Snowpark.\r\n- Salvar dados transformados em uma tabela do Snowflake.\r\n- Implementar uma planilha Python como um procedimento armazenado.\r\n\r\n### Próximas etapas\r\n\r\nPara aprender a processar dados de maneira incremental, orquestrar pipelines de dados com o Snowflake Tasks, implementá-los via pipelines de integração e entrega contínuas (continuous integration and continuous delivery, CI/CD), bem como usar a nova ferramenta de interface de linha de comando (command-line interface, CLI) do desenvolvedor Snowflake, além da extensão Visual Studio Code, continue a sua jornada e aprofunde os seus conhecimentos no quickstart [Pipelines de engenharia de dados com Snowpark Python](/en/developers/guides/data-engineering-pipelines-with-snowpark-python/).\r\n\r\n### Recursos relacionados\r\n\r\n- [Guia de desenvolvedor de planilhas Python](https://docs.snowflake.com/pt/developer-guide/snowpark/python/python-worksheets)\r\n- [Introdução à engenharia de dados e ML com Snowpark para Python](/guide/getting_started_with_dataengineering_ml_using_snowpark_python_ptbr/index.html)\r\n- [Avançado: guia de aprendizado de máquina com Snowpark para Python](/en/developers/guides/getting-started-snowpark-machine-learning/)\r\n- [Demonstrações do Snowpark para Python](https://github.com/Snowflake-Labs/snowpark-python-demos/blob/main/README.md)\r\n- [Guia do desenvolvedor de Snowpark para Python](https://docs.snowflake.com/pt/developer-guide/snowpark/python/index)",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-b386c8697f","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-d22d43055d",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-2759cb565f","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2024-05-30",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-47b261fb93","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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