Hello German,
The easiest and probably the fastest way is to use numpy array.
Simply pass the table into the array constructor:
import numpy as np
a = np.array(f.root.path.to.table)
If your table contains more than one type and you want to keep that
setup via a structured array, also pass in the dtype:
a = np.array(f.root.path.to.table, f.root.path.to.table.dtype)
Be Well
Anthony
On Mon, Feb 20, 2012 at 4:57 AM, German Ocampo <geroca...@gmail.com> wrote:
> Good morning
>
> Im new in Pytables and I have a simple question, please help me.
>
> I have a pytable created with four Float() columns, the number of rows
> in the table is around 500.000. I need to read all the complete table
> in fastest way possible to a numpy array in memory. Im using the
> following command:
>
> total =[ (x['col1'],x['col2'],x['col3'],x['col4'],) for x in
> row_relational]
>
> Question: Is there another command that allows to read in a fastest way?
>
> Many thanks
>
> German
>
>
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