Exercício pandas#11
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Aug 14, 2024
LarissaSchmillevitch
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- Identifiquei as colunas que contêm números, como 'Spotify Streams', 'YouTube Views', etc., e converta essas colunas para o tipo numérico se estiverem em outro formato. (Use replace() e astype())
- Corriji a coluna 'Release Date' para o formato datetime.
- Criei 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)
- Criei uma coluna 'Total Streams', somando os valores de 'Spotify Streams', 'YouTube Views', 'TikTok Views', 'Pandora Streams', e 'Soundcloud Streams'.
- Filtrei apenas as faixas onde a popularidade do Spotify ('Spotify Popularity') é maior que 80 e que tenham mais de 1 milhão de streams totais ('Total Streams').
- Salvei o DataFrame resultante em um novo arquivo JSON chamado 'faixas_filtradas.json'.
- Garanti que o arquivo foi salvo corretamente
| # Identificar colunas que contêm números e converter para o tipo numérico | ||
| for column in df.columns: | ||
| if df[column].dtype == "object": | ||
| df[column] = df[column].str.replace(",", "").astype(float, errors='ignore') |
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o erro está acontecendo porque está tentando converter colunas que de fato são strings
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