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
A função json.loads() em Python é utilizada para deserializar uma string contendo um objeto JSON para um objeto Python, como um dicionário, por exemplo. A palavra loads é uma abreviação de load string, o que significa que está carregando dados de uma string.
Quando precisamos importar dados de um arquivo JSON, utilizamos a função json.load(), sem o ‘s’ no final. Esta função é responsável por ler o conteúdo de um arquivo aberto, interpretar o JSON e converter em um objeto Python. Por isso, ao lidar com arquivos, usamos json.load(file), onde file é um objeto de arquivo (geralmente obtido através da função open()).
Portanto, a afirmação de que a função json.loads() é usada para ler os dados diretamente de um arquivo JSON está incorreta. O correto seria usar json.load() para essa finalidade. Esse detalhe é crucial para resolver questões de concursos públicos, onde a precisão técnica é fundamental.
Em um contexto de estudo para concursos, é importante lembrar que a linguagem de programação Python é rica em bibliotecas e funções específicas, e cada uma tem seu propósito definido. A diferença entre json.load() e json.loads() é um excelente exemplo de como um pequeno detalhe pode ter um grande impacto na maneira como o código é escrito e executado.