overriding partially an numpy array does not work












2















I tried to override a numpy array partially



enter image description here



does anyone know how to do that in such comfort indexing way?



Thanks!










share|improve this question



























    2















    I tried to override a numpy array partially



    enter image description here



    does anyone know how to do that in such comfort indexing way?



    Thanks!










    share|improve this question

























      2












      2








      2








      I tried to override a numpy array partially



      enter image description here



      does anyone know how to do that in such comfort indexing way?



      Thanks!










      share|improve this question














      I tried to override a numpy array partially



      enter image description here



      does anyone know how to do that in such comfort indexing way?



      Thanks!







      python numpy numpy-ndarray






      share|improve this question













      share|improve this question











      share|improve this question




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      asked Nov 20 '18 at 19:19









      malochomalocho

      3618




      3618
























          2 Answers
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          1














          Setup



          a = np.array([[1,2,3], [1,1,1], [1,1,1]])
          b = np.array([[888,888], [99, 99]])




          You are operating on a copy of the array, so the modifications are not persisted, use numpy.ix_ here:



          >>> a[np.ix_([1,2], [0,1])] = b
          >>> a
          array([[ 1, 2, 3],
          [888, 888, 1],
          [ 99, 99, 1]])





          share|improve this answer































            2














            You can also use this sort of indexing with the : separating your starting and ending indices:



            >>> a = np.array([[1,2,3], [1,1,1], [1,1,1]])
            # 1: and :2 all_indices_after_1 and all_indices_before_2 respectively
            >>> a[1:,:2] = np.array([[888,888], [99, 99]])
            >>> a
            array([[ 1, 2, 3],
            [888, 888, 1],
            [ 99, 99, 1]])





            share|improve this answer

























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              2 Answers
              2






              active

              oldest

              votes








              2 Answers
              2






              active

              oldest

              votes









              active

              oldest

              votes






              active

              oldest

              votes









              1














              Setup



              a = np.array([[1,2,3], [1,1,1], [1,1,1]])
              b = np.array([[888,888], [99, 99]])




              You are operating on a copy of the array, so the modifications are not persisted, use numpy.ix_ here:



              >>> a[np.ix_([1,2], [0,1])] = b
              >>> a
              array([[ 1, 2, 3],
              [888, 888, 1],
              [ 99, 99, 1]])





              share|improve this answer




























                1














                Setup



                a = np.array([[1,2,3], [1,1,1], [1,1,1]])
                b = np.array([[888,888], [99, 99]])




                You are operating on a copy of the array, so the modifications are not persisted, use numpy.ix_ here:



                >>> a[np.ix_([1,2], [0,1])] = b
                >>> a
                array([[ 1, 2, 3],
                [888, 888, 1],
                [ 99, 99, 1]])





                share|improve this answer


























                  1












                  1








                  1







                  Setup



                  a = np.array([[1,2,3], [1,1,1], [1,1,1]])
                  b = np.array([[888,888], [99, 99]])




                  You are operating on a copy of the array, so the modifications are not persisted, use numpy.ix_ here:



                  >>> a[np.ix_([1,2], [0,1])] = b
                  >>> a
                  array([[ 1, 2, 3],
                  [888, 888, 1],
                  [ 99, 99, 1]])





                  share|improve this answer













                  Setup



                  a = np.array([[1,2,3], [1,1,1], [1,1,1]])
                  b = np.array([[888,888], [99, 99]])




                  You are operating on a copy of the array, so the modifications are not persisted, use numpy.ix_ here:



                  >>> a[np.ix_([1,2], [0,1])] = b
                  >>> a
                  array([[ 1, 2, 3],
                  [888, 888, 1],
                  [ 99, 99, 1]])






                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered Nov 20 '18 at 19:26









                  user3483203user3483203

                  30.8k82454




                  30.8k82454

























                      2














                      You can also use this sort of indexing with the : separating your starting and ending indices:



                      >>> a = np.array([[1,2,3], [1,1,1], [1,1,1]])
                      # 1: and :2 all_indices_after_1 and all_indices_before_2 respectively
                      >>> a[1:,:2] = np.array([[888,888], [99, 99]])
                      >>> a
                      array([[ 1, 2, 3],
                      [888, 888, 1],
                      [ 99, 99, 1]])





                      share|improve this answer






























                        2














                        You can also use this sort of indexing with the : separating your starting and ending indices:



                        >>> a = np.array([[1,2,3], [1,1,1], [1,1,1]])
                        # 1: and :2 all_indices_after_1 and all_indices_before_2 respectively
                        >>> a[1:,:2] = np.array([[888,888], [99, 99]])
                        >>> a
                        array([[ 1, 2, 3],
                        [888, 888, 1],
                        [ 99, 99, 1]])





                        share|improve this answer




























                          2












                          2








                          2







                          You can also use this sort of indexing with the : separating your starting and ending indices:



                          >>> a = np.array([[1,2,3], [1,1,1], [1,1,1]])
                          # 1: and :2 all_indices_after_1 and all_indices_before_2 respectively
                          >>> a[1:,:2] = np.array([[888,888], [99, 99]])
                          >>> a
                          array([[ 1, 2, 3],
                          [888, 888, 1],
                          [ 99, 99, 1]])





                          share|improve this answer















                          You can also use this sort of indexing with the : separating your starting and ending indices:



                          >>> a = np.array([[1,2,3], [1,1,1], [1,1,1]])
                          # 1: and :2 all_indices_after_1 and all_indices_before_2 respectively
                          >>> a[1:,:2] = np.array([[888,888], [99, 99]])
                          >>> a
                          array([[ 1, 2, 3],
                          [888, 888, 1],
                          [ 99, 99, 1]])






                          share|improve this answer














                          share|improve this answer



                          share|improve this answer








                          edited Nov 20 '18 at 19:41

























                          answered Nov 20 '18 at 19:29









                          sacuLsacuL

                          30.1k41840




                          30.1k41840






























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