I have one array and i want to convert it into certain shape which I do not know how to do.
I tried, but it does not give me proper result.
Here is the array-: this is a numpy array
a=[[ [1,2,13],
[12,2,32],
[61,2,6],
[1,23,3],
[1,21,3],
[91,2,38] ]]
expected outputs-:
1. [[ [1,2],
[12,2],
[61,2],
[1,23],
[1,21],
[91,2] ]]
2. [ [1,2],
[12,2],
[61,2],
[1,23],
[1,21],
[91,2] ]
So the question can be boiled down to
"Given a 3D numpy array with the shape (1, 6, 3) how can we make a copy but a shape of (1, 6, 2) by removing the last indexed value from the innermost nested array?"
Array Indexing
The below example achieves this by slicing the original array (a) to return the desired structure.
import numpy as np
a = np.array([[[1,2,13],[12,2,32],[61,2,6],[1,23,3],[1,21,3],[91,2,38]]])
o = a[:,:,:2]
List Comprehension
The below makes use of a list comprehension applied to filter a down in the manner described above.
import numpy as np
a = np.array([[[1,2,13],[12,2,32],[61,2,6],[1,23,3],[1,21,3],[91,2,38]]])
o = np.array([[j[:2] for i in a for j in i]])
In each of the above examples o will refer to the following array (the first output you are asking for).
array([[[ 1, 2],
[12, 2],
[61, 2],
[ 1, 23],
[ 1, 21],
[91, 2]]])
Given o as defined by one of the above examples, your second sought output is accessible via o[0].
a[:, :2]
a[:,:,:2].
o[0].This will do
import numpy as np
a=[[ [1,2,13],
[12,2,32],
[61,2,6],
[1,23,3],
[1,21,3],
[91,2,38] ]]
outputs=list()
for i in a[0]:
outputs.append([i[0],i[1]])
print(np.array([outputs]))
""" OUTPUTS
[[[ 1 2]
[12 2]
[61 2]
[ 1 23]
[ 1 21]
[91 2]]]
"""
print(np.array(outputs))Instead of deriving output2 = output1[0] you could use squeeze method. It removes all the single - dimensional entries from your array
output1 = a[:,:,:2]
output2 = output1.squeeze()
This is a vizualization of a process for a better understanding:
print(a[:, 0:, 1:])