[SciPy-User] Select rows according to cell value
Oleksandr Huziy
guziy.sasha at gmail.com
Tue Nov 13 11:38:45 EST 2012
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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