python - Accessing a column of a NumPy array of structured arrays -


i've been given multidimensional numpy array, x looks this:

array([ array([  398.24475098,  -196.1497345 ,  -110.79341125, ..., -1937.22399902,        -6158.89355469,  1742.84399414], dtype=float32),        array([   32.27750397,  -171.73371887,  -342.6328125 , ..., -4727.4296875 ,        -4727.4296875 , -2545.10375977], dtype=float32),        array([  785.83660889,  -234.88890076,   140.49914551, ..., -7982.19482422,        -2127.640625  , -1434.77160645], dtype=float32),        ...,        array([   181.93313599,   -146.41413879,   -416.02978516, ...,         -4517.796875  ,  10491.84570312,  -6604.39550781], dtype=float32),        array([ -1.37602341e+02,   1.71733719e+02,   7.13068867e+00, ...,          8.60104688e+03,   1.39115127e+04,   3.31622314e+03], dtype=float32),        array([   453.17272949,    152.49285889,    260.41452026, ...,         19061.60742188,  11232.8046875 ,   7312.13964844], dtype=float32)], dtype=object)

i'm trying access each column (specifically i'm trying take standard deviation of each column). found this answer, , tried,

>>> x[:,0]

but returned error:

traceback (most recent call last):   file "<stdin>", line 1, in <module> indexerror: many indices array

is possible convert structured array simple 2d numpy array access columns? or there way access these columns directly?

thanks!

edit

some more information on array:

>>> x.shape (8685,) >>> x[0].shape  # same x[1], x[2], ... (3524,)

if it's help, used tree2array function in root_numpy package produce array.

not sure how doing it,but seems work me straight prompt

a=np.zeros((2,6),dtype=np.float32) >>> array([[ 0.,  0.,  0.,  0.,  0.,  0.],        [ 0.,  0.,  0.,  0.,  0.,  0.]], dtype=float32) >>> a[:,2]=1 >>> array([[ 0.,  0.,  1.,  0.,  0.,  0.],        [ 0.,  0.,  1.,  0.,  0.,  0.]], dtype=float32)

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