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\ No newline at end of file diff --git a/exercicios/para-casa/francitelma.py b/exercicios/para-casa/francitelma.py new file mode 100644 index 0000000..3d0bcee --- /dev/null +++ b/exercicios/para-casa/francitelma.py @@ -0,0 +1,44 @@ +#- Use o arquivo `mais_ouvidas_2024.csv` para análise. Lembre-se de garantir que o carregamento foi feito com sucesso. +import pandas as pd + +df = pd.read_csv("../../material/mais_ouvidas_2024.csv") +print (df.head()) + +#- Indentifique 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()) + +#print (df.columns) +convertendo_colunas = ['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[convertendo_colunas] = df[convertendo_colunas].replace({',': ''}, regex=True).astype(float) +print(df.dtypes) + +# - Corrija a coluna 'Release Date' para o formato datetime. + +df["Release Date"] = pd.to_datetime(df["Release Date"], format="mixed") +print(df["Release Date"]) + +# - 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) + +df['Streaming Popularity'] = (df['Spotify Popularity'] + df['YouTube Views'] + df['TikTok Likes'] + df['Shazam Counts']) / 4 +print(df["Streaming Popularity"].dtype) +print(df.head()) +#print(df.columns) + +# - Crie uma coluna 'Total Streams', somando os valores de 'Spotify Streams', 'YouTube Views', 'TikTok Views', 'Pandora Streams', e 'Soundcloud Streams'. +df['Total Streams'] = (df['Spotify Streams'] + df['YouTube Views'] + df['TikTok Views'] + df['Pandora Streams'] + df['Soundcloud Streams']) +print(df.head()) +#print(df.columns) + +# - Filtre 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'). + +filtradas = df[(df['Spotify Popularity'] > 80) & (df['Total Streams'] > 1_000_000)] +print(filtradas.head()) + +# - Salve o DataFrame resultante em um novo arquivo JSON chamado 'faixas_filtradas.json'. +filtradas.to_json("./faixas_filtradas.json", index= False) + +# - Garanta que o arquivo foi salvo corretamente +df = pd.read_json("../para_casa/mais_ouvidas_2024.json") +print (df.head())