Anthony, Uemit
Your answers were really usefull. Both options improve the time
compared to the previous one. Now the program is just taking to run
only 4% of the time compared to the previous code.
Many thanks!
German
Message: 5
Date: Mon, 20 Feb 2012 10:43:27 -0600
From: Anthony Scopatz <scop...@gmail.com>
Subject: Re: [Pytables-users] Question about reading a complete table.
To: Discussion list for PyTables
<pytables-users@lists.sourceforge.net>
Message-ID:
<capk-6t7jwetnffc97yhet3xuxfuqrp1xxj3le-agan-y_za...@mail.gmail.com>
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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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Message: 6
Date: Mon, 20 Feb 2012 18:15:57 +0100
From: ?mit Seren <uemit.se...@gmail.com>
Subject: Re: [Pytables-users] Question about reading a complete table.
To: Discussion list for PyTables
<pytables-users@lists.sourceforge.net>
Message-ID:
<canbyw4b26zmkf0olb8pxj+kdmnxyq0gs9nwfnkcrcrq7p4m...@mail.gmail.com>
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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 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
>>
>>
>> ------------------------------------------------------------------------------
>> Try before you buy = See our experts in action!
>> The most comprehensive online learning library for Microsoft developers
>> is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3,
>> Metro Style Apps, more. Free future releases when you subscribe now!
>> http://p.sf.net/sfu/learndevnow-dev2
>> _______________________________________________
>> Pytables-users mailing list
>> Pytables-users@lists.sourceforge.net
>> https://lists.sourceforge.net/lists/listinfo/pytables-users
>
>
>
> ------------------------------------------------------------------------------
> Try before you buy = See our experts in action!
> The most comprehensive online learning library for Microsoft developers
> is just $99.99! Visual Studio, SharePoint, SQL - plus HTML5, CSS3, MVC3,
> Metro Style Apps, more. Free future releases when you subscribe now!
> http://p.sf.net/sfu/learndevnow-dev2
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