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:
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
