Building a correlation matrix

Now that you've ranked albums, the strategy team wants to understand which metrics move together. The Spotify dataset has been enriched with streams_billions and streams_per_listener columns. Build a correlation matrix to spot strong and weak relationships between these numeric features.

polars is loaded as pl. The DataFrame spotify with additional columns is preloaded for you.

Este exercício faz parte do curso

Data Transformation with Polars

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Instruções do exercício

Pick five numeric columns: streams_billions, monthly_listeners, streams_per_listener, duration_ms, and popularity, and compute the correlation.
Add a metric column to the result for better readability.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Build a correlation matrix from selected columns
corr = spotify.____(
    "streams_billions",
    "monthly_listeners",
    "streams_per_listener",
    "duration_ms",
    "____",
).____()

# Add a metric column for row labels
result = corr.with_columns(pl.Series("____", corr.columns))

print(result)

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