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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
participants (3)
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Bob
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Sebastian Berg
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William Ray Wing