Cross addition in pandas











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.






python pandas







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.






    python pandas







    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.






      python pandas







      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




      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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