Fill blank cells with data above it





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I have data which looks like:



| ID       | Name      | Inv | Date       | Value | PO  | Type  | Rate  | Tax   | Integ |

|----------|-----------|-----|------------|-------|-----|-------|-------|-------|-------|

| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK  | State | 0.00% | 3000  | 0     |

|          |           |     |            | 0     | 0   | 0     | 5.00% | 10750 | 537.5 |


The software which generated this left banks for repeated data. The blank cells seen here should have the data seen in the cell above it.



The above data must ideally be like:



| ID       | Name      | Inv | Date       | Value | PO  | Type  | Rate  | Tax   | Integ |

|----------|-----------|-----|------------|-------|-----|-------|-------|-------|-------|

| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK  | State | 0.00% | 3000  | 0     |

| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK  | State | 5.00% | 10750 | 537.5 |


As you can see the columns 1-7 have been populated with the data seen above it. How do I do this in pandas.



I need to:




  • Identify blanks or NULLs in "ID" (column 1)

  • Populate that row: column 1 ot 7 with the data above that row.






python pandas







share|improve this question





























    2















    I have data which looks like:



    | ID       | Name      | Inv | Date       | Value | PO  | Type  | Rate  | Tax   | Integ |
    
|----------|-----------|-----|------------|-------|-----|-------|-------|-------|-------|
    
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK  | State | 0.00% | 3000  | 0     |
    
|          |           |     |            | 0     | 0   | 0     | 5.00% | 10750 | 537.5 |


    The software which generated this left banks for repeated data. The blank cells seen here should have the data seen in the cell above it.



    The above data must ideally be like:



    | ID       | Name      | Inv | Date       | Value | PO  | Type  | Rate  | Tax   | Integ |
    
|----------|-----------|-----|------------|-------|-----|-------|-------|-------|-------|
    
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK  | State | 0.00% | 3000  | 0     |
    
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK  | State | 5.00% | 10750 | 537.5 |


    As you can see the columns 1-7 have been populated with the data seen above it. How do I do this in pandas.



    I need to:




    • Identify blanks or NULLs in "ID" (column 1)

    • Populate that row: column 1 ot 7 with the data above that row.






    python pandas







    share|improve this question

























      2












      2








      2








      I have data which looks like:



      | ID       | Name      | Inv | Date       | Value | PO  | Type  | Rate  | Tax   | Integ |
      
|----------|-----------|-----|------------|-------|-----|-------|-------|-------|-------|
      
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK  | State | 0.00% | 3000  | 0     |
      
|          |           |     |            | 0     | 0   | 0     | 5.00% | 10750 | 537.5 |


      The software which generated this left banks for repeated data. The blank cells seen here should have the data seen in the cell above it.



      The above data must ideally be like:



      | ID       | Name      | Inv | Date       | Value | PO  | Type  | Rate  | Tax   | Integ |
      
|----------|-----------|-----|------------|-------|-----|-------|-------|-------|-------|
      
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK  | State | 0.00% | 3000  | 0     |
      
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK  | State | 5.00% | 10750 | 537.5 |


      As you can see the columns 1-7 have been populated with the data seen above it. How do I do this in pandas.



      I need to:




      • Identify blanks or NULLs in "ID" (column 1)

      • Populate that row: column 1 ot 7 with the data above that row.






      python pandas







      share|improve this question














      I have data which looks like:



      | ID       | Name      | Inv | Date       | Value | PO  | Type  | Rate  | Tax   | Integ |
      
|----------|-----------|-----|------------|-------|-----|-------|-------|-------|-------|
      
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK  | State | 0.00% | 3000  | 0     |
      
|          |           |     |            | 0     | 0   | 0     | 5.00% | 10750 | 537.5 |


      The software which generated this left banks for repeated data. The blank cells seen here should have the data seen in the cell above it.



      The above data must ideally be like:



      | ID       | Name      | Inv | Date       | Value | PO  | Type  | Rate  | Tax   | Integ |
      
|----------|-----------|-----|------------|-------|-----|-------|-------|-------|-------|
      
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK  | State | 0.00% | 3000  | 0     |
      
| DEADBEEF | CHEMICALS | 321 | 19-11-2017 | 14288 | UK  | State | 5.00% | 10750 | 537.5 |


      As you can see the columns 1-7 have been populated with the data seen above it. How do I do this in pandas.



      I need to:




      • Identify blanks or NULLs in "ID" (column 1)

      • Populate that row: column 1 ot 7 with the data above that row.






      python pandas




      python pandas






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 25 '18 at 7:05









      clmnoclmno

      779416




      779416
























          1 Answer
          1






          active

          oldest

          votes


















          2














          Use mask with forward filling missing values:



          df = df.mask(df == 0).ffill()


          Or:



          df = df.mask(df.isin(['', 0])).ffill()


          If want also change first row filled by missing values to 0 only for numeric columns:



          num = df.select_dtypes(np.number).columns
          
d = dict.fromkeys(num, 0)
          
print (d)
          
{'Inv': 0, 'Value': 0, 'Tax': 0, 'Integ': 0}
          

          
df = df.mask(df == 0).ffill().fillna(d)
          
print (df)
          
         ID       Name    Inv        Date    Value  PO   Type   Rate    Tax  
          
0  DEADBEEF  CHEMICALS  321.0  19-11-2017  14288.0  UK  State  0.00%   3000   
          
1  DEADBEEF  CHEMICALS  321.0  19-11-2017  14288.0   0      0  5.00%  10750   
          

          
   Integ  
          
0    0.0  
          
1  537.5





          share|improve this answer


























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            1 Answer
            1






            active

            oldest

            votes








            1 Answer
            1






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            2














            Use mask with forward filling missing values:



            df = df.mask(df == 0).ffill()


            Or:



            df = df.mask(df.isin(['', 0])).ffill()


            If want also change first row filled by missing values to 0 only for numeric columns:



            num = df.select_dtypes(np.number).columns
            
d = dict.fromkeys(num, 0)
            
print (d)
            
{'Inv': 0, 'Value': 0, 'Tax': 0, 'Integ': 0}
            

            
df = df.mask(df == 0).ffill().fillna(d)
            
print (df)
            
         ID       Name    Inv        Date    Value  PO   Type   Rate    Tax  
            
0  DEADBEEF  CHEMICALS  321.0  19-11-2017  14288.0  UK  State  0.00%   3000   
            
1  DEADBEEF  CHEMICALS  321.0  19-11-2017  14288.0   0      0  5.00%  10750   
            

            
   Integ  
            
0    0.0  
            
1  537.5





            share|improve this answer






























              2














              Use mask with forward filling missing values:



              df = df.mask(df == 0).ffill()


              Or:



              df = df.mask(df.isin(['', 0])).ffill()


              If want also change first row filled by missing values to 0 only for numeric columns:



              num = df.select_dtypes(np.number).columns
              
d = dict.fromkeys(num, 0)
              
print (d)
              
{'Inv': 0, 'Value': 0, 'Tax': 0, 'Integ': 0}
              

              
df = df.mask(df == 0).ffill().fillna(d)
              
print (df)
              
         ID       Name    Inv        Date    Value  PO   Type   Rate    Tax  
              
0  DEADBEEF  CHEMICALS  321.0  19-11-2017  14288.0  UK  State  0.00%   3000   
              
1  DEADBEEF  CHEMICALS  321.0  19-11-2017  14288.0   0      0  5.00%  10750   
              

              
   Integ  
              
0    0.0  
              
1  537.5





              share|improve this answer




























                2












                2








                2







                Use mask with forward filling missing values:



                df = df.mask(df == 0).ffill()


                Or:



                df = df.mask(df.isin(['', 0])).ffill()


                If want also change first row filled by missing values to 0 only for numeric columns:



                num = df.select_dtypes(np.number).columns
                
d = dict.fromkeys(num, 0)
                
print (d)
                
{'Inv': 0, 'Value': 0, 'Tax': 0, 'Integ': 0}
                

                
df = df.mask(df == 0).ffill().fillna(d)
                
print (df)
                
         ID       Name    Inv        Date    Value  PO   Type   Rate    Tax  
                
0  DEADBEEF  CHEMICALS  321.0  19-11-2017  14288.0  UK  State  0.00%   3000   
                
1  DEADBEEF  CHEMICALS  321.0  19-11-2017  14288.0   0      0  5.00%  10750   
                

                
   Integ  
                
0    0.0  
                
1  537.5





                share|improve this answer















                Use mask with forward filling missing values:



                df = df.mask(df == 0).ffill()


                Or:



                df = df.mask(df.isin(['', 0])).ffill()


                If want also change first row filled by missing values to 0 only for numeric columns:



                num = df.select_dtypes(np.number).columns
                
d = dict.fromkeys(num, 0)
                
print (d)
                
{'Inv': 0, 'Value': 0, 'Tax': 0, 'Integ': 0}
                

                
df = df.mask(df == 0).ffill().fillna(d)
                
print (df)
                
         ID       Name    Inv        Date    Value  PO   Type   Rate    Tax  
                
0  DEADBEEF  CHEMICALS  321.0  19-11-2017  14288.0  UK  State  0.00%   3000   
                
1  DEADBEEF  CHEMICALS  321.0  19-11-2017  14288.0   0      0  5.00%  10750   
                

                
   Integ  
                
0    0.0  
                
1  537.5






                share|improve this answer














                share|improve this answer



                share|improve this answer








                edited Nov 25 '18 at 7:15

























                answered Nov 25 '18 at 7:06









                jezraeljezrael

                363k26330412




                363k26330412
































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