[Python] How to apply function to multiple columns

I would like to replace with NaNs values that are more than 0.99 quantile and less than 0.01 quantile in the whole dataframe.
For now I found a way of doing so with one column, so I can do it one-at-a-time, but maybe there is possibility to apply the function to all the columns without ugly for-loops?

I also tried numpy implementation with masking, but since the length of the result is not constant, this does not seem like a proper solution to me.

Quantile replacer for one column that works:

train_pl.select(
pl.when(pl.col('B_14') > pl.col('B_14').quantile(0.99))
.then(float("nan"))
.otherwise(pl.col('B_14'))
)


And here are my numpy functions in case you need them:

def replace_high_quantile(arr, q = 0.99):
mask = arr <= np.quantile(arr, q)
return ma.masked_array(arr, ~mask).filled(np.nan)

def replace_low_quantile(arr, q = 0.01):
mask = arr >= np.quantile(arr, q)
return ma.masked_array(arr, ~mask).filled(np.nan)

def replace_both_quantiles(arr, low = 0.01, high = 0.99):
mask = (arr >= np.quantile(arr, low)) & (arr <= np.quantile(arr, high)
return ma.masked_array(arr, ~mask).filled(np.nan)

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