Similar to this, I am curious how to remove specific elements from every numpy array in a numpy array. My data is given in form of X below. I think this should work:
X = [[x1 x2 ... xn] [x1 x2 ... xn] ... [x1 x2 ... xn]]
X.shape
(n,|x|)
Y=numpy.delete(X[:],1)
I would think that Y should now be:
Y = [[x1 x3 ... xn] [x1 x3 ... xn] ... [x1 x3 ... xn]]
where Y.shape should equal (n-1,|y|=|x|), but it is not. What am I failing to grasp? My intention is to be able to remove all x2's (low correlation variable) in every array in X in order to send to a decision tree regressor. It would be even better if I could do this:
index = [ 1 3 7]
Y=numpy.delete(X[:],index)
which works if X is not a 'nested' numpy array. refer to response in link for:
array([1, 2, 3, 4, 5, 6, 7, 8, 9])
index
[2, 3, 6]
new_a = np.delete(a, index)
new_a
array([1, 2, 5, 6, 8, 9])
You need to apply np.delete
along an axis. Please refer to the third example in the documentation.
Y = np.delete(X, 1, axis=1)
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