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Subindo_ATV_CASA #19

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33 changes: 33 additions & 0 deletions exercicios/para-casa/Resolucao_ATVCASA
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import pandas as pd

file_path = 'C:/Users/alexc/OneDrive/Curso_Python/Reprograma_On33/Semana_11/on33-python-s09-pandas-numpy-I/material/mais_ouvidas_2024.csv'
df = pd.read_csv(file_path)
print(df.head())


numerical_columns = ['Spotify Streams', 'YouTube Views', 'TikTok Views', 'Pandora Streams', 'Soundcloud Streams',
'Spotify Popularity', 'TikTok Likes', 'Shazam Counts']


for col in numerical_columns:
df[col] = df[col].replace({',': ''}, regex=True).astype(float)
print(df[numerical_columns].dtypes)


df['Release Date'] = pd.to_datetime(df['Release Date'], format='%Y-%m-%d')
print(df['Release Date'].head())


df['Streaming Popularity'] = df[['Spotify Popularity', 'YouTube Views', 'TikTok Likes', 'Shazam Counts']].mean(axis=1)
print(df[['Spotify Popularity', 'YouTube Views', 'TikTok Likes', 'Shazam Counts', 'Streaming Popularity']].head())

df['Total Streams'] = df[['Spotify Streams', 'YouTube Views', 'TikTok Views', 'Pandora Streams', 'Soundcloud Streams']].sum(axis=1)
print(df[['Spotify Streams', 'YouTube Views', 'TikTok Views', 'Pandora Streams', 'Soundcloud Streams', 'Total Streams']].head())

filtered_df = df[(df['Spotify Popularity'] > 80) & (df['Total Streams'] > 1_000_000)]
print(filtered_df.head())

filtered_df.to_json('faixas_filtradas.json', orient='records', lines=True)

import os
print(os.path.exists('faixas_filtradas.json'))