Atividade de casa - S11 #8
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| if df[col].dtype == 'object': | ||
| df[col] = pd.to_numeric(df[col].str.replace(',', '', regex=True), errors='coerce') |
Collaborator
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já que se deu ao trabalho de criar a num_cols manualmente, porque está fazendo esse código e porque optou pelo uso do to_numeric() ao invés do astype()? 🤔
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| # Corrigindo a coluna 'Release Date' para o formato datetime | ||
| df['Release Date'] = pd.to_datetime(df['Release Date'], errors='coerce') |
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o que o parâmetro errors='coerce' faz?
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| for col in sum_cols: | ||
| if col not in df.columns: | ||
| print(f"Coluna {col} não encontrada no DataFrame.") |
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você converteu todas elas para float logo acima, não?
| print(df.dtypes) | ||
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| # Verificando se todas as colunas envolvidas na soma são numéricas | ||
| sum_cols = ['Spotify Streams', 'YouTube Views', 'TikTok Views', 'Pandora Streams', 'Soundcloud Streams'] |
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Suggested change
| sum_cols = ['Spotify Streams', 'YouTube Views', 'TikTok Views', 'Pandora Streams', 'Soundcloud Streams'] | |
| streams_cols = ['Spotify Streams', 'YouTube Views', 'TikTok Views', 'Pandora Streams', 'Soundcloud Streams'] |
| filtered_df.to_json('faixas_filtradas.json', orient='records', lines=True) | ||
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| # Verificando se o arquivo JSON foi salvo corretamente | ||
| import os |
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os imports vão no começo do código
manuellysuzik
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