Creating reusable expressions

Now that you can transform columns, you want to understand which artists have a dedicated fanbase by calculating streams per listener. Rather than rewriting this formula each time, store the calculation as a Polars expression variable that you can reuse across multiple DataFrames.

polars is loaded as pl. The DataFrame spotify is available with album streaming data.

本练习是课程的一部分

Data Transformation with Polars

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练习说明

Create an expression that divides streams by monthly_listeners.
Apply the expression variable and sort by streams_per_listener in descending order.

交互式实操练习

通过完成这段示例代码来试试这个练习。

# Create a reusable expression for streams per listener
streams_per_listener_expr = (
    (pl.col("____") / pl.col("____"))
    .alias("streams_per_listener")
)

# Apply the expression and sort by streams_per_listener
result = spotify.with_columns(____).sort(
    "streams_per_listener", descending=____
)

print(result.head())

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