Numpy question regarding accessing elements












0















So I have created an array from 3 nested lists (atleast I think it is an array from 3 lists), and I want to access the three diagonal elements in it. I have the array created, but how do I access the three diagonal elements in it?



from numpy import *

test1 = arange(27).reshape(3,3,3)

test1


Result:



array([[[ 0,  1,  2],

      [ 3,  4,  5],

      [ 6,  7,  8]],



     [[ 9, 10, 11],

      [12, 13, 14],

      [15, 16, 17]],



     [[18, 19, 20],

      [21, 22, 23],

      [24, 25, 26]]])





python numpy







share|improve this question





























    0















    So I have created an array from 3 nested lists (atleast I think it is an array from 3 lists), and I want to access the three diagonal elements in it. I have the array created, but how do I access the three diagonal elements in it?



    from numpy import *
    
test1 = arange(27).reshape(3,3,3)
    
test1


    Result:



    array([[[ 0,  1,  2],
    
      [ 3,  4,  5],
    
      [ 6,  7,  8]],
    

    
     [[ 9, 10, 11],
    
      [12, 13, 14],
    
      [15, 16, 17]],
    

    
     [[18, 19, 20],
    
      [21, 22, 23],
    
      [24, 25, 26]]])





    python numpy







    share|improve this question



























      0












      0








      0








      So I have created an array from 3 nested lists (atleast I think it is an array from 3 lists), and I want to access the three diagonal elements in it. I have the array created, but how do I access the three diagonal elements in it?



      from numpy import *
      
test1 = arange(27).reshape(3,3,3)
      
test1


      Result:



      array([[[ 0,  1,  2],
      
      [ 3,  4,  5],
      
      [ 6,  7,  8]],
      

      
     [[ 9, 10, 11],
      
      [12, 13, 14],
      
      [15, 16, 17]],
      

      
     [[18, 19, 20],
      
      [21, 22, 23],
      
      [24, 25, 26]]])





      python numpy







      share|improve this question
















      So I have created an array from 3 nested lists (atleast I think it is an array from 3 lists), and I want to access the three diagonal elements in it. I have the array created, but how do I access the three diagonal elements in it?



      from numpy import *
      
test1 = arange(27).reshape(3,3,3)
      
test1


      Result:



      array([[[ 0,  1,  2],
      
      [ 3,  4,  5],
      
      [ 6,  7,  8]],
      

      
     [[ 9, 10, 11],
      
      [12, 13, 14],
      
      [15, 16, 17]],
      

      
     [[18, 19, 20],
      
      [21, 22, 23],
      
      [24, 25, 26]]])





      python numpy




      python numpy






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Nov 22 '18 at 2:24







      blargh

















      asked Nov 22 '18 at 1:58









      blarghblargh

      63




      63
























          2 Answers
          2






          active

          oldest

          votes


















          0














          Here is a list comprehension approach:



          >>> [np.diagonal(i) for i in test1]
          
[array([0, 4, 8]), array([ 9, 13, 17]), array([18, 22, 26])]





          share|improve this answer


























          • Sorry I had to re edit my code, and the np.diag(test1) does not work now : (

            – blargh
            Nov 22 '18 at 2:24



















          0














          There are several ways to achieve your goal. Here, I will highlight the use of a boolean mask.



          First create the boolean 3x3 identity matrix : i.e. the diagonal is True whilst 2. every off diagonal entry is False.
          Then overlay the boolean mask over your original ndarray to get the diagonals.



          import numpy as np
          
test1 = np.arange(27).reshape(3,3,3)
          

          
>>> diag = np.eye(3, dtype=bool)
          
>>> test1[:, diag]
          
array([[ 0,  4,  8],
          
       [ 9, 13, 17],
          
       [18, 22, 26]])


          As you can see, this gives a 2d array where each row is the corresponding diagonal of the zeroth, first and second
          2d array in your 3d array.



          As an aside, avoid import *, it is the cause of many a headache because if destroys the namespace abstraction you
          have. In the above example, what if numpy had a diag function or variable defined? same if you import another package after numpy and it happens to have it's own arange function, you will looes numpy's arange function.
          Prefer explicit imports to star imports.






          share|improve this answer
























          • I see! Thank you and I will def keep the asterisk in mind. I have a follow up question, what is a constant array?

            – blargh
            Nov 22 '18 at 3:39











          • Please accept the solution if it answers your question. A constant array is an array that is constant i.e. in a mathematical context it can be an array of coefficients where the coefficients are constant.

            – Xero Smith
            Nov 22 '18 at 3:46











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          2 Answers
          2






          active

          oldest

          votes








          2 Answers
          2






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          0














          Here is a list comprehension approach:



          >>> [np.diagonal(i) for i in test1]
          
[array([0, 4, 8]), array([ 9, 13, 17]), array([18, 22, 26])]





          share|improve this answer


























          • Sorry I had to re edit my code, and the np.diag(test1) does not work now : (

            – blargh
            Nov 22 '18 at 2:24
















          0














          Here is a list comprehension approach:



          >>> [np.diagonal(i) for i in test1]
          
[array([0, 4, 8]), array([ 9, 13, 17]), array([18, 22, 26])]





          share|improve this answer


























          • Sorry I had to re edit my code, and the np.diag(test1) does not work now : (

            – blargh
            Nov 22 '18 at 2:24














          0












          0








          0







          Here is a list comprehension approach:



          >>> [np.diagonal(i) for i in test1]
          
[array([0, 4, 8]), array([ 9, 13, 17]), array([18, 22, 26])]





          share|improve this answer















          Here is a list comprehension approach:



          >>> [np.diagonal(i) for i in test1]
          
[array([0, 4, 8]), array([ 9, 13, 17]), array([18, 22, 26])]






          share|improve this answer














          share|improve this answer



          share|improve this answer








          edited Nov 22 '18 at 2:43

























          answered Nov 22 '18 at 2:10









          Siong Thye GohSiong Thye Goh

          908412




          908412













          • Sorry I had to re edit my code, and the np.diag(test1) does not work now : (

            – blargh
            Nov 22 '18 at 2:24



















          • Sorry I had to re edit my code, and the np.diag(test1) does not work now : (

            – blargh
            Nov 22 '18 at 2:24

















          Sorry I had to re edit my code, and the np.diag(test1) does not work now : (

          – blargh
          Nov 22 '18 at 2:24





          Sorry I had to re edit my code, and the np.diag(test1) does not work now : (

          – blargh
          Nov 22 '18 at 2:24













          0














          There are several ways to achieve your goal. Here, I will highlight the use of a boolean mask.



          First create the boolean 3x3 identity matrix : i.e. the diagonal is True whilst 2. every off diagonal entry is False.
          Then overlay the boolean mask over your original ndarray to get the diagonals.



          import numpy as np
          
test1 = np.arange(27).reshape(3,3,3)
          

          
>>> diag = np.eye(3, dtype=bool)
          
>>> test1[:, diag]
          
array([[ 0,  4,  8],
          
       [ 9, 13, 17],
          
       [18, 22, 26]])


          As you can see, this gives a 2d array where each row is the corresponding diagonal of the zeroth, first and second
          2d array in your 3d array.



          As an aside, avoid import *, it is the cause of many a headache because if destroys the namespace abstraction you
          have. In the above example, what if numpy had a diag function or variable defined? same if you import another package after numpy and it happens to have it's own arange function, you will looes numpy's arange function.
          Prefer explicit imports to star imports.






          share|improve this answer
























          • I see! Thank you and I will def keep the asterisk in mind. I have a follow up question, what is a constant array?

            – blargh
            Nov 22 '18 at 3:39











          • Please accept the solution if it answers your question. A constant array is an array that is constant i.e. in a mathematical context it can be an array of coefficients where the coefficients are constant.

            – Xero Smith
            Nov 22 '18 at 3:46
















          0














          There are several ways to achieve your goal. Here, I will highlight the use of a boolean mask.



          First create the boolean 3x3 identity matrix : i.e. the diagonal is True whilst 2. every off diagonal entry is False.
          Then overlay the boolean mask over your original ndarray to get the diagonals.



          import numpy as np
          
test1 = np.arange(27).reshape(3,3,3)
          

          
>>> diag = np.eye(3, dtype=bool)
          
>>> test1[:, diag]
          
array([[ 0,  4,  8],
          
       [ 9, 13, 17],
          
       [18, 22, 26]])


          As you can see, this gives a 2d array where each row is the corresponding diagonal of the zeroth, first and second
          2d array in your 3d array.



          As an aside, avoid import *, it is the cause of many a headache because if destroys the namespace abstraction you
          have. In the above example, what if numpy had a diag function or variable defined? same if you import another package after numpy and it happens to have it's own arange function, you will looes numpy's arange function.
          Prefer explicit imports to star imports.






          share|improve this answer
























          • I see! Thank you and I will def keep the asterisk in mind. I have a follow up question, what is a constant array?

            – blargh
            Nov 22 '18 at 3:39











          • Please accept the solution if it answers your question. A constant array is an array that is constant i.e. in a mathematical context it can be an array of coefficients where the coefficients are constant.

            – Xero Smith
            Nov 22 '18 at 3:46














          0












          0








          0







          There are several ways to achieve your goal. Here, I will highlight the use of a boolean mask.



          First create the boolean 3x3 identity matrix : i.e. the diagonal is True whilst 2. every off diagonal entry is False.
          Then overlay the boolean mask over your original ndarray to get the diagonals.



          import numpy as np
          
test1 = np.arange(27).reshape(3,3,3)
          

          
>>> diag = np.eye(3, dtype=bool)
          
>>> test1[:, diag]
          
array([[ 0,  4,  8],
          
       [ 9, 13, 17],
          
       [18, 22, 26]])


          As you can see, this gives a 2d array where each row is the corresponding diagonal of the zeroth, first and second
          2d array in your 3d array.



          As an aside, avoid import *, it is the cause of many a headache because if destroys the namespace abstraction you
          have. In the above example, what if numpy had a diag function or variable defined? same if you import another package after numpy and it happens to have it's own arange function, you will looes numpy's arange function.
          Prefer explicit imports to star imports.






          share|improve this answer













          There are several ways to achieve your goal. Here, I will highlight the use of a boolean mask.



          First create the boolean 3x3 identity matrix : i.e. the diagonal is True whilst 2. every off diagonal entry is False.
          Then overlay the boolean mask over your original ndarray to get the diagonals.



          import numpy as np
          
test1 = np.arange(27).reshape(3,3,3)
          

          
>>> diag = np.eye(3, dtype=bool)
          
>>> test1[:, diag]
          
array([[ 0,  4,  8],
          
       [ 9, 13, 17],
          
       [18, 22, 26]])


          As you can see, this gives a 2d array where each row is the corresponding diagonal of the zeroth, first and second
          2d array in your 3d array.



          As an aside, avoid import *, it is the cause of many a headache because if destroys the namespace abstraction you
          have. In the above example, what if numpy had a diag function or variable defined? same if you import another package after numpy and it happens to have it's own arange function, you will looes numpy's arange function.
          Prefer explicit imports to star imports.







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Nov 22 '18 at 3:06









          Xero SmithXero Smith

          8861514




          8861514













          • I see! Thank you and I will def keep the asterisk in mind. I have a follow up question, what is a constant array?

            – blargh
            Nov 22 '18 at 3:39











          • Please accept the solution if it answers your question. A constant array is an array that is constant i.e. in a mathematical context it can be an array of coefficients where the coefficients are constant.

            – Xero Smith
            Nov 22 '18 at 3:46



















          • I see! Thank you and I will def keep the asterisk in mind. I have a follow up question, what is a constant array?

            – blargh
            Nov 22 '18 at 3:39











          • Please accept the solution if it answers your question. A constant array is an array that is constant i.e. in a mathematical context it can be an array of coefficients where the coefficients are constant.

            – Xero Smith
            Nov 22 '18 at 3:46

















          I see! Thank you and I will def keep the asterisk in mind. I have a follow up question, what is a constant array?

          – blargh
          Nov 22 '18 at 3:39





          I see! Thank you and I will def keep the asterisk in mind. I have a follow up question, what is a constant array?

          – blargh
          Nov 22 '18 at 3:39













          Please accept the solution if it answers your question. A constant array is an array that is constant i.e. in a mathematical context it can be an array of coefficients where the coefficients are constant.

          – Xero Smith
          Nov 22 '18 at 3:46





          Please accept the solution if it answers your question. A constant array is an array that is constant i.e. in a mathematical context it can be an array of coefficients where the coefficients are constant.

          – Xero Smith
          Nov 22 '18 at 3:46


















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