[SciPy-User] Select rows according to cell value

[Oleksandr Huziy]

Yeps, I admit with pandas it appears much easier

import pandas
df = pandas.read_csv("tmp/data.txt", sep="\\s")
df = df.dropna(axis = 1)

#df.index = df["alt"]
selection = df.select(lambda i: df.ix[i, "alt"] in altitudes)
print selection

cheers
--
Oleksandr (Sasha) Huziy



2012/11/13 Oleksandr Huziy <guziy.sasha at gmail.com>

> I am not sure if this way is easier thsn yours, but here is what I wpuld do
>
> tol = 0.01
> all_alts = data[:,0]
> print all_alts
> all_alts_temp = np.vstack([all_alts]*len(altitudes))
> print all_alts_temp
>
> sel_alts_temp = np.vstack([altitudes]*len(all_alts)).transpose()
> print sel_alts_temp
> sel_pattern = np.any( np.abs(all_alts_temp - sel_alts_temp) < tol, axis =
> 0)
> print sel_pattern
> print data
> print data[sel_pattern,:]
>
>
> Cheers
> --
> Oleksandr (Sasha) Huziy
>
>
>
>
> 2012/11/13 Andreas Hilboll <lists at hilboll.de>
>
>> Am Di 13 Nov 2012 17:07:19 CET schrieb Juan Luis Cano Rodríguez:
>> > I am loading some tabular data of the form
>> >
>> >   alt    temp    press    dens
>> >   10.0    223.3    26500    0.414
>> >   10.5    220.0    24540    0.389
>> >   11.0    216.8    22700    0.365
>> >   11.5    216.7    20985    0.337
>> >   12.0    216.7    19399    0.312
>> >   12.5    216.7    17934    0.288
>> >   13.0    216.7    16579    0.267
>> >   13.5    216.7    15328    0.246
>> >   14.0    216.7    14170    0.228
>> >
>> > into an ordinary NumPy array using np.loadtxt. I would like though to
>> > select the rows according to the altitude level, that is:
>> >
>> >     >>> data = np.loadtxt('data.txt', skiprows=1)
>> >     >>> altitudes = [10.5, 11.5, 14.0]
>> >     >>> d = ...  # some simple syntax involving data and altitudes
>> >     >>> d
>> >     10.5    220.0    24540    0.389
>> >     11.5    216.7    20985    0.337
>> >     14.0    216.7    14170    0.228
>> >
>> > I have tried a cumbersome expression which traverses all the array,
>> > then uses a list comprehension, converts to an array... but I'm sure
>> > there must be a simpler way. I've also looked at argwhere. Or maybe I
>> > should use pandas?
>> >
>> > Thank you in advance.
>> >
>> >
>> > _______________________________________________
>> > SciPy-User mailing list
>> > SciPy-User at scipy.org
>> > http://mail.scipy.org/mailman/listinfo/scipy-user
>>
>> +1 for using pandas
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>
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