# [SciPy-User] Select rows according to cell value

Juan Luis Cano Rodríguez juanlu001@gmail....
Tue Nov 13 11:41:46 CST 2012

```Actually I arrived to a couple of one-liners:

d = np.take(data, [np.argwhere(data[:, 0] == a).flatten()[0] for a in
altitudes], axis=0)

or

d = np.array([data[data[:, 0] == a][0] for a in altitudes])

I find them sort of ugly but maybe it's the only way. The same way I'd do

data[[1, 3, 8]]

to retrieve the first, third and eighth I'd like to do

data[np.magic_indices(altitudes)]

On Tue, Nov 13, 2012 at 6:02 PM, Oleksandr Huziy <guziy.sasha@gmail.com>wrote:

> Yeps, I admit with pandas it appears much easier
>
> import pandas
> 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@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@hilboll.de>
>>
>>> Am Di 13 Nov 2012 17:07:19 CET schrieb Juan Luis Cano Rodríguez:
>>> >
>>> >   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@scipy.org
>>> > http://mail.scipy.org/mailman/listinfo/scipy-user
>>>
>>> +1 for using pandas
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>>>
>>
>>
>
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