FYI, I recoded np.loadtxt to handle missing data, automatic name
definition and conversion functions, as a merge of np.loadtxt and
mlab.csv2rec. You can access the code here:
https://code.launchpad.net/~pierregm/numpy/numpy_addons
Hopefully these functions will make it to numpy at one point or another.
Note also that you are not limited to recarrays: you can use what's
called a flexible-type arrays, which still gives the possibility to
access individual fields by keys, without the overload of recarrays
(where fields can also be accessed as attributes). For example:
>>> x=np.array([(1,10.), (2,20.)], dtype=[('A',int),('B',float)])
>>>x['A']
array([1, 2])
On Jan 3, 2009, at 12:59 PM, Patrick Marsh wrote:
> In my limited opinion, numpy's loadtxt is the way to go. Loadtxt
> doesn't care about the headerYou can read in the arrays like this:
>
> # read in all 5 columns as text
> col1, col2, col3, col4, col5 = np.loadtxt(filename, dtype=dtype,
> unpack=True)
>
> or if you want to skip the column headings and read in just a specific
> data type of just the last column
>
> # read in only column 5, as a specific dtype, and exclude the column
> 5 heading
> col5_no_header = np.loadtxt(filename, skiprows=1, usecols=(5),
> dtype=dtype, unpack=True)
>
>
> -Patrick
>
>
>
>
>
>
> On Sat, Jan 3, 2009 at 11:39 AM, antonv <vasilescu_an...@yahoo.com>
> wrote:
>>
>> I am plotting the data in those csv files and the forst 4 columns
>> in the
>> files have the same title but the 5th has the name based on the
>> date and
>> time so it would be unique in each of the files. As I have about
>> 600 files
>> to batch process, adjusting my script manually is not an option.
>>
>> The way I have it for one test file is:
>>
>> r = mlab.csv2rec('test.csv')
>> #i know that the column name for the 5th column is 'htsgw_12191800'
>> #so to read the data in the 5th column i just use:
>> z = r.htsgw_12191800
>>
>> What i need is to be able to get that data by specifying the column
>> number
>> as that stays the same in all files.
>>
>> I'll look at numpy but I hope there is a simpler way.
>>
>> Thanks,
>> Anton
>>
>>
>>
>> Patrick Marsh-2 wrote:
>>>
>>> I'm not sure what you are needing it for, but I would suggest
>>> looking
>>> into numpy's loadtxt function. You can use this to load the csv
>>> data
>>> into numpy arrays and pass the resulting arrays arround.
>>>
>>> -Patrick
>>>
>>>
>>>
>>>
>>>
>>>
>>> On Sat, Jan 3, 2009 at 11:21 AM, antonv
>>> <vasilescu_an...@yahoo.com> wrote:
>>>>
>>>> Hi all,
>>>>
>>>> I have a lot of csv files to process, all of them with the same
>>>> number of
>>>> columns. The only issue is that each file has a unique column
>>>> name for
>>>> the
>>>> fourth column.
>>>>
>>>> All the csv2rec examples I found are using the r.column_name
>>>> format to
>>>> access the data in that column which is of no use for me because
>>>> of the
>>>> unique names. Is there a way to access that data using the column
>>>> number?
>>>> I
>>>> bet this should be something simple but I cannot figure it out...
>>>>
>>>> Thanks in advance,
>>>> Anton
>>>> --
>>>> View this message in context:
>>>> http://www.nabble.com/csv2rec-column-names-tp21267055p21267055.html
>>>> Sent from the matplotlib - users mailing list archive at
>>>> Nabble.com.
>>>>
>>>>
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>>
>> --
>> View this message in context:
>> http://www.nabble.com/csv2rec-column-names-tp21267055p21267232.html
>> Sent from the matplotlib - users mailing list archive at Nabble.com.
>>
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