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.

本练习是课程的一部分

Data Transformation with Polars

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练习说明

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.

交互式实操练习

通过完成这段示例代码来试试这个练习。

# 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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