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
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
I guess using the slice operator on the table should probably also
load the entire table into memory:
a = f.root.path.to.table[:]
This will return a structured array tough.
On Mon, Feb 20, 2012 at 5:43 PM, Anthony Scopatz scop...@gmail.com wrote:
Hello German,
The easiest and probably the
On Feb 20, 2012, at 6:15 PM, Ümit Seren wrote:
I guess using the slice operator on the table should probably also
load the entire table into memory:
a = f.root.path.to.table[:]
Much, much better :)
This will return a structured array tough.
Yes, but nothing that cannot be solved:
a =
