Hello
On Mon, 02 Jul 2007, Barry Wark wrote:
> I have the potential to add OS X Server Intel (64-bit) and OS X Intel
> (32-bit) to the list, if I can convince my boss that the security risk
Sounds good. We could definitely use these platforms.
> (including DOS from compile times) is minimal. I'
On 7/8/07, Vincent Nijs <[EMAIL PROTECTED]> wrote:
Thanks for looking into this Torgil! I agree that this is a much more
complicated setup. I'll check if there is anything I can do on the data
end.
Otherwise I'll go with Timothy's suggestion and read in numbers as floats
and convert to int later
FWIW
>>> n,dt=descr[0]
>>> new_dt=dt.replace('f','i')
>>> descr[0]=(n,new_dt)
>>> data=ra.col1.astype(new_dt)
>>> ra.dtype=N.dtype(descr)
>>> ra.col1=data
//Torgil
On 7/9/07, Vincent Nijs <[EMAIL PROTECTED]> wrote:
>
> Tim,
>
> I do want to auto-detect. Reading numbers in as floats is probably
Thanks for looking into this Torgil! I agree that this is a much more
complicated setup. I'll check if there is anything I can do on the data end.
Otherwise I'll go with Timothy's suggestion and read in numbers as floats
and convert to int later as needed.
Vincent
On 7/8/07 5:40 PM, "Torgil Sven
Tim,
I do want to auto-detect. Reading numbers in as floats is probably not a
huge penalty.
Is there an easy way to change the type of one column in a recarray that you
know?
I tried this:
ra.col1 = ra.col1.astype(i¹)
but that didn¹t seem to work. I assume that means you would have to create
Question: If you do ignore the int's initially, once the rec array is in
memory, would there be a quick way to check if the floats could pass as
int's? This may seem like a backwards approach but it might be 'safer' if
you really want to preserve the int's.
In your case the floats don't pass as
On 7/8/07, Vincent Nijs <[EMAIL PROTECTED]> wrote:
Torgil,
The function seems to work well and is slightly faster than your previous
version (about 1/6th faster).
Yes, I do have columns that start with, what looks like, int's and then
turn
out to be floats. Something like below (col6).
da
Torgil,
The function seems to work well and is slightly faster than your previous
version (about 1/6th faster).
Yes, I do have columns that start with, what looks like, int's and then turn
out to be floats. Something like below (col6).
data = [['col1', 'col2', 'col3', 'col4', 'col5', 'col6']
Hi
I stumble on these types of problems from time to time so I'm
interested in efficient solutions myself.
Do you have a column which starts with something suitable for int on
the first row (without decimal separator) but has decimals further
down?
This will be little tricky to support. One solu
Stefan,
No worries. I thought it was something like that. Any thoughts on my
other questions? I'd love to have some ammunition to take to my boss.
Thanks,
Barry
On 7/7/07, stefan <[EMAIL PROTECTED]> wrote:
>
> On Mon, 2 Jul 2007 17:26:15 -0700, "Barry Wark" <[EMAIL PROTECTED]>
> wrote:
> > On a
On 7/8/07, Timothy Hochberg <[EMAIL PROTECTED]> wrote:
On 7/8/07, Torgil Svensson <[EMAIL PROTECTED]> wrote:
> Given that both your script and the mlab version preloads the whole
> file before calling numpy constructor I'm curious how that compares in
> speed to using numpy's fromiter function
I am not (yet) very familiar with much of the functionality introduced in
your script Torgil (izip, imap, etc.), but I really appreciate you taking
the time to look at this!
The program stopped with the following error:
File "load_iter.py", line 48, in
convert_row=lambda r: tuple(fn(x) for
On 7/8/07, Torgil Svensson <[EMAIL PROTECTED]> wrote:
Given that both your script and the mlab version preloads the whole
file before calling numpy constructor I'm curious how that compares in
speed to using numpy's fromiter function on your data. Using fromiter
should improve on memory usage (~
Given that both your script and the mlab version preloads the whole
file before calling numpy constructor I'm curious how that compares in
speed to using numpy's fromiter function on your data. Using fromiter
should improve on memory usage (~50% ?).
The drawback is for string columns where we don
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