Exercicio_S11_Jessica - #6
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| #Corrija a coluna 'Release Date' para o formato datetime. | ||
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| df['Release Date'] = pd.to_datetime(df['Release Date'], errors='coerce') |
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o que faz definir o parâmetro errors='coerce' faz?
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| #Crie uma nova coluna chamada 'Streaming Popularity', que seja a média da popularidade nas plataformas 'Spotify Popularity', 'YouTube Views', 'TikTok Likes', e 'Shazam Counts'. (lembrem-se que só é possível calcular médias e fazer operações matemáticas com tipos númericos) | ||
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| df['Streaming Popularity'] = (df['Spotify Popularity'] + df['YouTube Views'] + df['TikTok Likes'] + df['Shazam Counts']) / 4 |
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É possível calcular a média as colunas pela função sum()
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| #Crie uma coluna 'Total Streams', somando os valores de 'Spotify Streams', 'YouTube Views', 'TikTok Views', 'Pandora Streams', e 'Soundcloud Streams'. | ||
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| df['Total Streams'] = (df['Spotify Streams'] + df['YouTube Views'] + df['TikTok Views'] + df['Pandora Streams'] + df['Soundcloud Streams']) |
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é possível obter a soma de varias colunas com o uso do sum()
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| #Salve o DataFrame resultante em um novo arquivo JSON chamado 'faixas_filtradas.json'. | ||
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| df.to_json('./faixas_filtradas.json', orient='records', lines=True) |
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O que fazem esses parâmetros que você enviou e porque usou?
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