> On Jul 24, 2017, at 10:37 AM, Bob <bobmerh...@gmail.com> 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). >
Have you considered using Pandas? Assuming I understand what you are trying to do, that nested list could read directly into a Pandas 2D data frame. Extracting the first element of each column (or row) is then fast and efficient. Bill > How can I do that for large data without resorting to loops? Here's the > result I want with a loop: > > 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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
