import polars as pl
import numpy as np

df_sim = pl.DataFrame({
   "daily_n": [1000, 2000, 3000, 4000],
   "prob": [.5, .5, .5, .6],
   "size": 1
   })

df_sim = df_sim.with_columns(
  pl.struct(["daily_n", "prob", "size"])
  .map_elements(lambda x: 
      np.random.binomial(n=x['daily_n'], p=x['prob'], size=x['size']))
  .cast(pl.Int32)
  .alias('events')
  )

df_sim

However the following code would fail with the message
“TypeError: float() argument must be a string or a number, not ‘Expr'”

df_sim.with_columns(
  np.random.binomial(n=col('daily_n'), p=col('prob'), size=col('size'))
  .alias('events')
  )

Why do some functions require use of struct(), map_elements() and lambda, while others do not?

In my case below I am able to simply refer to polars columns as function arguments by using col().

def local_double(x):
  return(2*x)

df_ab.with_columns(rev_2x = local_double(col("revenue")))