PyCafe - Vizro - Analyzing Music Trends with Vizro

app.py
requirements.txt
app.py
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# Vizro is an open-source toolkit for creating modular data visualization applications.
# check out https://github.com/mckinsey/vizro for more info about Vizro
# and checkout https://vizro.readthedocs.io/en/stable/ for documentation.
import pandas as pd
import vizro.plotly.express as px
from vizro import Vizro
import vizro.models as vm


df = pd.read_csv('https://raw.githubusercontent.com/plotly/Figure-Friday/main/2024/week-34/dataset.csv')

top_10_songs = df.nlargest(20, 'popularity')
df['tempo_bins'] = pd.cut(df['tempo'], bins=[60, 80, 100, 120, 140, 160], labels=['60-80', '80-100', '100-120', '120-140', '140-160'])
avg_valence = df.groupby('tempo_bins')['valence'].mean().reset_index()
avg_danceability = df.groupby('track_genre')['danceability'].mean().reset_index()
top_genres = avg_danceability.sort_values(by='danceability', ascending=False).head(10)
page = vm.Page(
    title="Music analysis with Vizro",
    layout=vm.Layout(grid=[[1, 0], [1,2]]),
    components=[
       vm.Graph(id="heatmap", figure=px.density_heatmap(df, x='tempo', y='valence', z='popularity', 
                         labels={'tempo': 'Tempo (BPM)', 'valence': 'Happiness', 'popularity': 'Popularity'},)
                ),
        vm.Graph(id="line", figure=px.line(avg_valence, x='tempo_bins', y='valence',
              labels={'tempo_bins': 'Tempo Ranges (BPM)', 'valence': 'Average Valence (Happiness)'})
                ),
        vm.Graph(id="bar", figure = px.bar(top_genres, x='track_genre', y='danceability'))
    ],

    controls=[],
)

dashboard = vm.Dashboard(pages=[page], theme="vizro_light")
Vizro().build(dashboard).run()
Py.Cafe

vizro-music-trend-analysis

Analyzing Music Trends with Vizro
