Nice, hank you for the approximate timeline!
On Mon, Jun 17, 2019 at 1:15 AM Micah Kornfield
wrote:
> Hi Bogdan,
>
>> Alright, so speaking of serialization of pyarrow.Table vs Feather, if
>> they are pretty much the same, but arrow alone shouldn't
>> be used to long-storage, is this also the cas
Hi Bogdan,
> Alright, so speaking of serialization of pyarrow.Table vs Feather, if they
> are pretty much the same, but arrow alone shouldn't
> be used to long-storage, is this also the case for Feather or can it be a
> valid option for my case?
Per Wes's e-mail on similar thread[1], once we rea
Hello. Thanks for the reply!
On Sun, Jun 16, 2019 at 8:40 AM Wes McKinney wrote:
> hi Micah,
>
> On Sun, Jun 16, 2019 at 12:16 AM Micah Kornfield
> wrote:
> >
> > Hi Bogdan,
> > I'm not an expert here but answers based on my understanding are below:
> >
> > 1) Is there something I'm missing i
hi Micah,
On Sun, Jun 16, 2019 at 12:16 AM Micah Kornfield wrote:
>
> Hi Bogdan,
> I'm not an expert here but answers based on my understanding are below:
>
> 1) Is there something I'm missing in understanding difference between
> > serializing dataframe directly using PyArrow and serializing
> >
Hi Bogdan,
I'm not an expert here but answers based on my understanding are below:
1) Is there something I'm missing in understanding difference between
> serializing dataframe directly using PyArrow and serializing
> `pyarrow.Table`, Table shines in case dataframes mostly consists of
> strings, w
Trying to come up with a solution for quick Pandas dataframes serialization
and long-storage. Dataframe content is tabular, but provided by user, can
be arbitrary, so might both completely text columns and completely
numeric/boolean columns.
## Main goals are:
* Serialize dataframe as quickly as
