AtividadeS11 #20
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AtividadeS11 #20
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| df ['Spotify Streams'] = df['Spotify Streams'].str.replace(',', '').fillna('0').astype(int) | ||
| print(df['Spotify Streams']) |
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essa era a única coluna que deveria ser convertida? por que tomou a decisão para substituir os valores nulos por 0 e porque converter a int e não float?
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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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indicaria o uso do mean() para calcular a média dos valores das colunas
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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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indicaria o uso do sum() para a soma dos valores das colunas
| print (filtered_df) | ||
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| #- Salve o DataFrame resultante em um novo arquivo JSON chamado 'faixas_filtradas.json'. | ||
| filtered_df.to_csv('./faixas_filtradas.csv', index=False) |
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acredito que deveria ser salvo em json, então o uso da função é to_json
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atividade semana 11, conversões, filtros e etc...