Consider Polars behavior for columns with only np.nan values

[petetronic]

Following on from #4307, and the associated fix #4329, we should review how a Polars series of only np.nan values should summarize. Our treatment for Polars differs from Pandas:

Polars with np.nan series

import polars as pl
import numpy as np
pl_nan = pl.DataFrame({"missing": pl.Series([np.nan] * 5, dtype=pl.Float64)})

Pandas with np.nan series

import pandas as pd
import numpy as np

pd_nan = pd.DataFrame({"missing": pd.Series([np.nan] * 5, dtype="float64")})

[dfalbel]

There's some reasoning for this beahvior in the polars docs:

https://docs.pola.rs/user-guide/expressions/missing-data/#notanumber-or-nan-values

Basically, unlike pandas they don't treat NaN's as missing data. It seems like NaN is the expected behavior for the summary stats here. Note also the %missing is 0. We would want to change the behavior of %missing here too.