[Python] Polars: What is the fastest way to loop over Polars DataFrame columns to apply...

Is there a preferred way to loop and apply functions to Polars columns?

Here is a pandas example of what I am trying to do:

df1 = pl.DataFrame(
{
"A": np.random.rand(10),
"B": np.random.rand(10),
"C": np.random.rand(10)
}
)
df2 = pl.DataFrame(
{
"X1": np.random.rand(10),
"X2": np.random.rand(10),
"X3": np.random.rand(10)
}
)


# pandas code

# this is just a weighted sum of df2, where the weights are from df1
df1.to_pandas().apply(
lambda weights: df2.to_pandas().mul(weights, axis=0).sum() / weights.sum(), axis=0,
result_type='expand'
)


A B C
X1 0.647355 0.705358 0.692214
X2 0.500439 0.416325 0.384294
X3 0.601890 0.606301 0.577076

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