Series have support for
universal functions (ufuncs).
Element-wise functions such as
np.div(), etc. all work with
almost zero overhead.
However, as a
Polars-specific remark: missing values are a separate bitmask and are not
NumPy. This can lead to a window function or a
flawed or incomplete results.
Series to a
NumPy array with the
Missing values will be replaced by
np.nan during the conversion. If the
not include missing values, or those values are not desired anymore, the
method can be used instead, providing a zero-copy
NumPy array of the data.