On 22/01/2012 19:58, Arnaud Delobelle wrote:
On 22 January 2012 16:09, MRAB wrote:
On 22/01/2012 15:39, Arnaud Delobelle wrote:
[...]
Or more succintly (but not tested):
sections = [
("3", "section_1")
("5", "section_2")
("\xFF", "section_3")
]
with open(input_path)
The grep solution is not cross-platform, and not really an answer to a
question about python.
The by-line iteration examples are inefficient and bad practice from a
numpy/vectorization perspective.
I would advice to do it the numpythonic way (untested code):
breakpoints = [3, 5, 7]
data = np.loa
On Jan 22, 9:37 pm, Roy Smith wrote:
> On Jan 22, 2012, at 2:34 PM, Tim Chase wrote:
>
> > On 01/22/12 13:26, Roy Smith wrote:
> >>> If you wanted to do it in one pass using standard unix
> >>> tools, you can use:
>
> >>> sed -n -e'/^[0-2]/w first-three.txt' -e'/^[34]/w
> >>> next-two.txt' -e'/^[5
On 22 January 2012 16:09, MRAB wrote:
> On 22/01/2012 15:39, Arnaud Delobelle wrote:
[...]
>> Or more succintly (but not tested):
>>
>>
>> sections = [
>> ("3", "section_1")
>> ("5", "section_2")
>> ("\xFF", "section_3")
>> ]
>>
>> with open(input_path) as input_file:
>> lines = it
On Jan 22, 2012, at 2:34 PM, Tim Chase wrote:
> On 01/22/12 13:26, Roy Smith wrote:
>>> If you wanted to do it in one pass using standard unix
>>> tools, you can use:
>>>
>>> sed -n -e'/^[0-2]/w first-three.txt' -e'/^[34]/w
>>> next-two.txt' -e'/^[5-7]/w next-three.txt'
>>>
>> I stand humbled.
>
On 01/22/12 13:26, Roy Smith wrote:
If you wanted to do it in one pass using standard unix
tools, you can use:
sed -n -e'/^[0-2]/w first-three.txt' -e'/^[34]/w
next-two.txt' -e'/^[5-7]/w next-three.txt'
I stand humbled.
In all likelyhood, you stand *younger*, not so much humbled ;-)
-tkc
I stand humbled.
On Jan 22, 2012, at 2:25 PM, Tim Chase wrote:
> On 01/22/12 08:45, Roy Smith wrote:
>> I would do this with standard unix tools:
>>
>> grep '^[012]' input.txt> first-three-seconds.txt
>> grep '^[34]' input.txt> next-two-seconds.txt
>> grep '^[567]' input.txt> next-three-secon
On 01/22/12 08:45, Roy Smith wrote:
I would do this with standard unix tools:
grep '^[012]' input.txt> first-three-seconds.txt
grep '^[34]' input.txt> next-two-seconds.txt
grep '^[567]' input.txt> next-three-seconds.txt
Sure, it makes three passes over the data, but for 20 MB of data, you
co
On Jan 22, 6:56 pm, MRAB wrote:
> On 22/01/2012 16:17, Yigit Turgut wrote:
> [snip]
>
>
>
>
>
>
>
> > On Jan 22, 5:39 pm, Arnaud Delobelle wrote:
> [snip]
> >> Or more succintly (but not tested):
>
> >> sections = [
> >> ("3", "section_1")
> >> ("5", "section_2")
> >> ("\xFF", "s
On 22/01/2012 16:17, Yigit Turgut wrote:
[snip]
On Jan 22, 5:39 pm, Arnaud Delobelle wrote:
[snip]
Or more succintly (but not tested):
sections = [
("3", "section_1")
("5", "section_2")
("\xFF", "section_3")
]
with open(input_path) as input_file:
lines = iter(input_fi
On Jan 22, 4:45 pm, Roy Smith wrote:
> In article
> ,
> Yigit Turgut wrote:
> > Hi all,
>
> > I have a text file approximately 20mb in size and contains about one
> > million lines. I was doing some processing on the data but then the
> > data rate increased and it takes very long time to proce
On 22/01/2012 15:39, Arnaud Delobelle wrote:
On 22 January 2012 15:19, MRAB wrote:
Here's a solution in Python 3:
input_path = "..."
section_1_path = "..."
section_2_path = "..."
section_3_path = "..."
with open(input_path) as input_file:
try:
line = next(input_file)
On 22 January 2012 15:19, MRAB wrote:
> Here's a solution in Python 3:
>
> input_path = "..."
> section_1_path = "..."
> section_2_path = "..."
> section_3_path = "..."
>
> with open(input_path) as input_file:
> try:
> line = next(input_file)
>
> # Copy section 1.
> with o
On 22/01/2012 14:32, Yigit Turgut wrote:
Hi all,
I have a text file approximately 20mb in size and contains about one
million lines. I was doing some processing on the data but then the
data rate increased and it takes very long time to process. I import
using numpy.loadtxt, here is a fragment o
In article
,
Yigit Turgut wrote:
> Hi all,
>
> I have a text file approximately 20mb in size and contains about one
> million lines. I was doing some processing on the data but then the
> data rate increased and it takes very long time to process. I import
> using numpy.loadtxt, here is a frag
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