PR Atividade11Semana11#9
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| # colunas com tipagem incorreta: 'Spotify Streams', 'Spotify Playlist Count', 'Spotify Playlist Reach', 'YouTube Views', 'YouTube Likes', 'TikTok Posts','TikTok Likes', 'TikTok Views', 'YouTube Playlist Reach','AirPlay Spins', 'SiriusXM Spins', 'Deezer Playlist Reach', 'Pandora Streams', 'Pandora Track Stations', 'Soundcloud Streams', 'Shazam Counts'], | ||
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| colunas_tipagem = ['Spotify Streams', 'Spotify Playlist Count', 'Spotify Playlist Reach', 'YouTube Views', 'YouTube Likes', 'TikTok Posts','TikTok Likes', 'TikTok Views', 'YouTube Playlist Reach','AirPlay Spins', 'SiriusXM Spins', 'Deezer Playlist Reach', 'Pandora Streams', 'Pandora Track Stations', 'Soundcloud Streams', 'Shazam Counts'] | ||
| df_mais_ouvidas[colunas_tipagem] = df_mais_ouvidas[colunas_tipagem].apply(lambda x: x.str.replace(',', '').astype('float')) |
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porque usar o apply se o str.replace + astype já faz o que é preciso?
Author
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utilizei para a construção do lambda e ele procurar item a item do df. Não sei se é a melhor forma, mas entendi que utilizar o lambda aqui para que ele percorresse todos os itens
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Sim, o apply é muito bom para diversos casos e você fez o uso do ˋlambdaˋ corretamente. Mas nesse caso adicionar o apply não adianta muita coisa , mesmo sem ele o resultado é o mesmo. 😃
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| # print(df_mais_ouvidas.dtypes) | ||
| df_mais_ouvidas['Release Date'] = pd.to_datetime(df_mais_ouvidas['Release Date'], format='mixed') |
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também é possível fazer com:
Suggested change
| df_mais_ouvidas['Release Date'] = pd.to_datetime(df_mais_ouvidas['Release Date'], format='mixed') | |
| df_mais_ouvidas['Release Date'] = pd.to_datetime(df_mais_ouvidas['Release Date'], format='%m/%d/%YYYY') |
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| colunas_popularidade = ['Spotify Popularity', 'YouTube Views', 'TikTok Likes', 'Shazam Counts'] | ||
| df_mais_ouvidas['Streaming Popularity'] = df_mais_ouvidas[colunas_popularidade].median(axis=1) |
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mean() = média
median() = mediana
Suggested change
| df_mais_ouvidas['Streaming Popularity'] = df_mais_ouvidas[colunas_popularidade].median(axis=1) | |
| df_mais_ouvidas['Streaming Popularity'] = df_mais_ouvidas[colunas_popularidade].mean(axis=1) |
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Entrega da atividade 11 da semana 11 Profa. Manuelly - Turma ON33
Tema: ETL com Pandas [tipagem, filtragem]