hi Mitar -- to Robert's point, we aren't sure which code path you are
referring to.
Perhaps related, I'm interested in handling Python pickling for
"other" kinds of Python objects when converting to or from the Arrow
format. So "Python object" would be defined as a user defined type
that's embedde
Slightly off-topic, but the recent work on PEP 574 (*) should allow
efficient serialization of Pandas dataframes (**) with standard pickle
(or the pickle5 backport). Experimental support for pickle5 has
already been merged in Arrow and Numpy (and Pandas uses Numpy as its
storage backend). My pe
How are you serializing the dataframe? If you use *pyarrow.serialize(df)*,
then each column should be serialized separately and numeric columns will
be handled efficiently.
On Thu, Oct 18, 2018 at 9:10 PM Mitar wrote:
> Hi!
>
> It seems that if a DataFrame contains both numeric and object column
Hi!
It seems that if a DataFrame contains both numeric and object columns,
the whole DataFrame is pickled and not that only object columns are
pickled? Is this right? Are there any plans to improve this?
Mitar
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