Cross addition in pandas











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0
down vote

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How to apply cross addition (OR) in my pandas dataframe like below.



Input:



   A  B  C  D
0 0 1 0 1


Output:



   A  B  C  D
0 0 1 0 1
1 1 1 1 1
2 0 1 0 1
3 1 1 1 1


So far I can achieve using this,



cols=df.columns
n=len(cols)
df1=pd.concat([df]*n,ignore_index=True).eq(1)
df2= pd.concat([df.T]*n,axis=1,ignore_index=True).eq(1)
df2.columns=cols
df2=df2.reset_index(drop=True)
print (df1|df2).astype(int)


I think there is much more simpler way to handle this case.










share|improve this question


























    up vote
    0
    down vote

    favorite












    How to apply cross addition (OR) in my pandas dataframe like below.



    Input:



       A  B  C  D
    0 0 1 0 1


    Output:



       A  B  C  D
    0 0 1 0 1
    1 1 1 1 1
    2 0 1 0 1
    3 1 1 1 1


    So far I can achieve using this,



    cols=df.columns
    n=len(cols)
    df1=pd.concat([df]*n,ignore_index=True).eq(1)
    df2= pd.concat([df.T]*n,axis=1,ignore_index=True).eq(1)
    df2.columns=cols
    df2=df2.reset_index(drop=True)
    print (df1|df2).astype(int)


    I think there is much more simpler way to handle this case.










    share|improve this question
























      up vote
      0
      down vote

      favorite









      up vote
      0
      down vote

      favorite











      How to apply cross addition (OR) in my pandas dataframe like below.



      Input:



         A  B  C  D
      0 0 1 0 1


      Output:



         A  B  C  D
      0 0 1 0 1
      1 1 1 1 1
      2 0 1 0 1
      3 1 1 1 1


      So far I can achieve using this,



      cols=df.columns
      n=len(cols)
      df1=pd.concat([df]*n,ignore_index=True).eq(1)
      df2= pd.concat([df.T]*n,axis=1,ignore_index=True).eq(1)
      df2.columns=cols
      df2=df2.reset_index(drop=True)
      print (df1|df2).astype(int)


      I think there is much more simpler way to handle this case.










      share|improve this question













      How to apply cross addition (OR) in my pandas dataframe like below.



      Input:



         A  B  C  D
      0 0 1 0 1


      Output:



         A  B  C  D
      0 0 1 0 1
      1 1 1 1 1
      2 0 1 0 1
      3 1 1 1 1


      So far I can achieve using this,



      cols=df.columns
      n=len(cols)
      df1=pd.concat([df]*n,ignore_index=True).eq(1)
      df2= pd.concat([df.T]*n,axis=1,ignore_index=True).eq(1)
      df2.columns=cols
      df2=df2.reset_index(drop=True)
      print (df1|df2).astype(int)


      I think there is much more simpler way to handle this case.







      python pandas






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked yesterday









      Mohamed Thasin ah

      3,13031236




      3,13031236
























          2 Answers
          2






          active

          oldest

          votes

















          up vote
          3
          down vote



          accepted










          You can use numpy | operation with broadcast as:



          data = df.values
          df = pd.DataFrame((data.T | data), columns=df.columns)


          Or using np.logical_or as:



          df = pd.DataFrame(np.logical_or(data,data.T).astype(int), columns=df.columns)




          print(df)

          A B C D
          0 0 1 0 1
          1 1 1 1 1
          2 0 1 0 1
          3 1 1 1 1





          share|improve this answer




























            up vote
            1
            down vote













            Numpy solution:



            First extract first row to 1d array with iloc and then broadcast by a[:, None] for change shape to Mx1:



            a = df.iloc[0].values
            df = pd.DataFrame(a | a[:, None], columns=df.columns)
            print (df)
            A B C D
            0 0 1 0 1
            1 1 1 1 1
            2 0 1 0 1
            3 1 1 1 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








              up vote
              3
              down vote



              accepted










              You can use numpy | operation with broadcast as:



              data = df.values
              df = pd.DataFrame((data.T | data), columns=df.columns)


              Or using np.logical_or as:



              df = pd.DataFrame(np.logical_or(data,data.T).astype(int), columns=df.columns)




              print(df)

              A B C D
              0 0 1 0 1
              1 1 1 1 1
              2 0 1 0 1
              3 1 1 1 1





              share|improve this answer

























                up vote
                3
                down vote



                accepted










                You can use numpy | operation with broadcast as:



                data = df.values
                df = pd.DataFrame((data.T | data), columns=df.columns)


                Or using np.logical_or as:



                df = pd.DataFrame(np.logical_or(data,data.T).astype(int), columns=df.columns)




                print(df)

                A B C D
                0 0 1 0 1
                1 1 1 1 1
                2 0 1 0 1
                3 1 1 1 1





                share|improve this answer























                  up vote
                  3
                  down vote



                  accepted







                  up vote
                  3
                  down vote



                  accepted






                  You can use numpy | operation with broadcast as:



                  data = df.values
                  df = pd.DataFrame((data.T | data), columns=df.columns)


                  Or using np.logical_or as:



                  df = pd.DataFrame(np.logical_or(data,data.T).astype(int), columns=df.columns)




                  print(df)

                  A B C D
                  0 0 1 0 1
                  1 1 1 1 1
                  2 0 1 0 1
                  3 1 1 1 1





                  share|improve this answer












                  You can use numpy | operation with broadcast as:



                  data = df.values
                  df = pd.DataFrame((data.T | data), columns=df.columns)


                  Or using np.logical_or as:



                  df = pd.DataFrame(np.logical_or(data,data.T).astype(int), columns=df.columns)




                  print(df)

                  A B C D
                  0 0 1 0 1
                  1 1 1 1 1
                  2 0 1 0 1
                  3 1 1 1 1






                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered yesterday









                  Sandeep Kadapa

                  5,341426




                  5,341426
























                      up vote
                      1
                      down vote













                      Numpy solution:



                      First extract first row to 1d array with iloc and then broadcast by a[:, None] for change shape to Mx1:



                      a = df.iloc[0].values
                      df = pd.DataFrame(a | a[:, None], columns=df.columns)
                      print (df)
                      A B C D
                      0 0 1 0 1
                      1 1 1 1 1
                      2 0 1 0 1
                      3 1 1 1 1





                      share|improve this answer



























                        up vote
                        1
                        down vote













                        Numpy solution:



                        First extract first row to 1d array with iloc and then broadcast by a[:, None] for change shape to Mx1:



                        a = df.iloc[0].values
                        df = pd.DataFrame(a | a[:, None], columns=df.columns)
                        print (df)
                        A B C D
                        0 0 1 0 1
                        1 1 1 1 1
                        2 0 1 0 1
                        3 1 1 1 1





                        share|improve this answer

























                          up vote
                          1
                          down vote










                          up vote
                          1
                          down vote









                          Numpy solution:



                          First extract first row to 1d array with iloc and then broadcast by a[:, None] for change shape to Mx1:



                          a = df.iloc[0].values
                          df = pd.DataFrame(a | a[:, None], columns=df.columns)
                          print (df)
                          A B C D
                          0 0 1 0 1
                          1 1 1 1 1
                          2 0 1 0 1
                          3 1 1 1 1





                          share|improve this answer














                          Numpy solution:



                          First extract first row to 1d array with iloc and then broadcast by a[:, None] for change shape to Mx1:



                          a = df.iloc[0].values
                          df = pd.DataFrame(a | a[:, None], columns=df.columns)
                          print (df)
                          A B C D
                          0 0 1 0 1
                          1 1 1 1 1
                          2 0 1 0 1
                          3 1 1 1 1






                          share|improve this answer














                          share|improve this answer



                          share|improve this answer








                          edited yesterday

























                          answered yesterday









                          jezrael

                          307k20243317




                          307k20243317






























                               

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