On Mon, 2017-07-24 at 16:37 +0200, Bob wrote: > Hello, > > I created the following array by converting it from a nested list: > > a = np.array([np.array([ > 17.56578416, 16.82712825, 16.57992292, > 15.83534836]), > np.array([ 17.9002445 > , 17.35024876, 16.69733472, 15.78809856]), > np.array([ > 17.90086839, 17.64315136, 17.40653009, 17.26346787, > 16.99901931, 16.87787178, 16.68278558, 16.56006419, > 16.43672445]), > np.array([ 17.91147242, 17.2770623 , 17.0320501 , > 16.73729491, 16.4910479 ])], dtype=object) > > I wish to slice the first element of each sub-array so I can perform > basic statistics (mean, sd, etc...0). > > How can I do that for large data without resorting to loops? Here's > the > result I want with a loop: >
Arrays of arrays are not very nice in these regards, you could use np.frompyfunc/np.vectorize together with `operator.getitem` to avoid the loop. It probably will not be much faster though. - Sebastian > s = np.zeros(4) > for i in np.arange(4): > s[i] = a[i][0] > > array([ 17.56578416, 17.9002445 , 17.90086839, 17.91147242]) > > Thank you > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
signature.asc
Description: This is a digitally signed message part
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
