Explore Spotify Data
Load the Spotify tracks dataset and explore audio features, popularity patterns, and genre distributions.
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Explore Spotify Data
python • Step 1
Load the Spotify tracks dataset and explore audio features, popularity patterns, and genre distributions.
pandasedavisualization
Learning Objectives
- →Load and inspect the Spotify dataset
- →Understand audio features (danceability, energy, valence, tempo)
- →Visualize popularity distributions across genres
Dataset
View on Kaggle Matches Kaggle
~114k Spotify tracks with audio features + genre labels.
*bleep* Load the dataset in the browser terminal:
from pyodide.http import open_url
import pandas as pd
df = pd.read_csv(open_url("/datasets/dataset.csv"))
print(df.shape)
print(df.columns.tolist()) Running locally? The file inside the Kaggle download is already named dataset.csv — drop it into the notebook's working directory and the code above runs unchanged (swap open_url(...) for "dataset.csv").
Dataset Columns
track_idartistsalbum_nametrack_namepopularityduration_msdanceabilityenergyloudnessspeechinessacousticnessinstrumentalnesslivenessvalencetempotrack_genre- Write your solution in the Try It Yourself editor below — matplotlib/seaborn figures and pandas tables render inline.
- Stuck? Expand a section and click ↑ Send to editor to stack its snippet into the editor, notebook-style.
- Hit ▶ Run (or ⌘↵) — once your output matches the expected result, the step is marked solved.
- Use ↺ Reset to restore the starter code at any time.
Try It Yourself
Python
Loading editor...
*bleep* Output will appear here after running your code. Figures from matplotlib/seaborn render inline.
Expected output:
Shape: Popularity stats:
0/2
Export to GitHub
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