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
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
On Jul 24, 2017, at 10:37 AM, Bob bobmerhebi@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
-
Bob -
Sebastian Berg -
William Ray Wing