Python: Changing values in a DataFrame











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0
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I'm new to python and pandas and I need some ideas. Say I have the following DataFrame:



0 1 2 3 4 5

1 5 5 5 5 5

2 5 5 5 5 5

3 5 5 5 5 5

4 5 5 5 5 5


I want to iterate through each row and change the values of specific columns. Say I wanted to change all of the values in columns (2,3,4) to a 3.



This is what I've tried, am I going down the right path?



for row in df.iterrows():

        for col in range(2, 4):

            df.set_value('row', 'col', 3)


EDIT:
Thanks for the responses. The simple solutions are obvious, but what if I wanted to change the values to this... for example:



0  1  2  3  4  5

1  1  2  3  4  5

2  6  7  8  9  10

3  11 12 13 14 15

4  16 17 18 19 20





python pandas dataframe







share|improve this question
























  • Just assign a value to column df[2]=3, df[3]=3, df[4]=3
    – Sociopath
    Nov 19 at 11:37












  • df[[2, 3, 4]] = 3 ?
    – jpp
    Nov 19 at 11:38










  • There is really no reason to loop. you can call each column and asign the value. Is there anything else you will want, apart from asigning a value to the column?
    – MEdwin
    Nov 19 at 11:38

















up vote
0
down vote

favorite












I'm new to python and pandas and I need some ideas. Say I have the following DataFrame:



0 1 2 3 4 5

1 5 5 5 5 5

2 5 5 5 5 5

3 5 5 5 5 5

4 5 5 5 5 5


I want to iterate through each row and change the values of specific columns. Say I wanted to change all of the values in columns (2,3,4) to a 3.



This is what I've tried, am I going down the right path?



for row in df.iterrows():

        for col in range(2, 4):

            df.set_value('row', 'col', 3)


EDIT:
Thanks for the responses. The simple solutions are obvious, but what if I wanted to change the values to this... for example:



0  1  2  3  4  5

1  1  2  3  4  5

2  6  7  8  9  10

3  11 12 13 14 15

4  16 17 18 19 20





python pandas dataframe







share|improve this question
























  • Just assign a value to column df[2]=3, df[3]=3, df[4]=3
    – Sociopath
    Nov 19 at 11:37












  • df[[2, 3, 4]] = 3 ?
    – jpp
    Nov 19 at 11:38










  • There is really no reason to loop. you can call each column and asign the value. Is there anything else you will want, apart from asigning a value to the column?
    – MEdwin
    Nov 19 at 11:38















up vote
0
down vote

favorite









up vote
0
down vote

favorite











I'm new to python and pandas and I need some ideas. Say I have the following DataFrame:



0 1 2 3 4 5

1 5 5 5 5 5

2 5 5 5 5 5

3 5 5 5 5 5

4 5 5 5 5 5


I want to iterate through each row and change the values of specific columns. Say I wanted to change all of the values in columns (2,3,4) to a 3.



This is what I've tried, am I going down the right path?



for row in df.iterrows():

        for col in range(2, 4):

            df.set_value('row', 'col', 3)


EDIT:
Thanks for the responses. The simple solutions are obvious, but what if I wanted to change the values to this... for example:



0  1  2  3  4  5

1  1  2  3  4  5

2  6  7  8  9  10

3  11 12 13 14 15

4  16 17 18 19 20





python pandas dataframe







share|improve this question















I'm new to python and pandas and I need some ideas. Say I have the following DataFrame:



0 1 2 3 4 5

1 5 5 5 5 5

2 5 5 5 5 5

3 5 5 5 5 5

4 5 5 5 5 5


I want to iterate through each row and change the values of specific columns. Say I wanted to change all of the values in columns (2,3,4) to a 3.



This is what I've tried, am I going down the right path?



for row in df.iterrows():

        for col in range(2, 4):

            df.set_value('row', 'col', 3)


EDIT:
Thanks for the responses. The simple solutions are obvious, but what if I wanted to change the values to this... for example:



0  1  2  3  4  5

1  1  2  3  4  5

2  6  7  8  9  10

3  11 12 13 14 15

4  16 17 18 19 20





python pandas dataframe




python pandas dataframe






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 19 at 11:50

























asked Nov 19 at 11:35









embedded.95

186




186












  • Just assign a value to column df[2]=3, df[3]=3, df[4]=3
    – Sociopath
    Nov 19 at 11:37












  • df[[2, 3, 4]] = 3 ?
    – jpp
    Nov 19 at 11:38










  • There is really no reason to loop. you can call each column and asign the value. Is there anything else you will want, apart from asigning a value to the column?
    – MEdwin
    Nov 19 at 11:38




















  • Just assign a value to column df[2]=3, df[3]=3, df[4]=3
    – Sociopath
    Nov 19 at 11:37












  • df[[2, 3, 4]] = 3 ?
    – jpp
    Nov 19 at 11:38










  • There is really no reason to loop. you can call each column and asign the value. Is there anything else you will want, apart from asigning a value to the column?
    – MEdwin
    Nov 19 at 11:38


















Just assign a value to column df[2]=3, df[3]=3, df[4]=3
– Sociopath
Nov 19 at 11:37






Just assign a value to column df[2]=3, df[3]=3, df[4]=3
– Sociopath
Nov 19 at 11:37














df[[2, 3, 4]] = 3 ?
– jpp
Nov 19 at 11:38




df[[2, 3, 4]] = 3 ?
– jpp
Nov 19 at 11:38












There is really no reason to loop. you can call each column and asign the value. Is there anything else you will want, apart from asigning a value to the column?
– MEdwin
Nov 19 at 11:38






There is really no reason to loop. you can call each column and asign the value. Is there anything else you will want, apart from asigning a value to the column?
– MEdwin
Nov 19 at 11:38














2 Answers
2






active

oldest

votes

















up vote
2
down vote













you can do this



df.iloc[:,1] = 3 #columns 2 

df.iloc[:,2] = 3 

df.iloc[:,3] = 3





share|improve this answer






























    up vote
    2
    down vote













    If you are using a loop when working with dataframes, you are almost always not on the right track.



    For this you can use a vectorized assignment:



    df[[2, 3, 4]] = 3


    Example:



    df = pd.DataFrame({1: [1, 2], 2:  [1, 2]})
    
print(df)
    
#     1  2
    
#  0  1  1
    
#  1  2  2
    

    
df[[1, 2]] = 3
    

    
print(df)
    
#     1  2
    
#  0  3  3
    
#  1  3  3





    share|improve this answer























    • Yes, this would be correct. But what if I wanted to apply this to 2000 rows of data, where the assigned values are different for each row. It now becomes more complicated, hence the loop. I created this dumbed down example, but I assume the principle would be the same.
      – embedded.95
      Nov 19 at 11:45










    • @embedded.95 Then that is an entirely different question which has been asked and answered many times before. Use pandas indexing or np.where.
      – DeepSpace
      Nov 19 at 11:49













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    2 Answers
    2






    active

    oldest

    votes








    2 Answers
    2






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes








    up vote
    2
    down vote













    you can do this



    df.iloc[:,1] = 3 #columns 2 
    
df.iloc[:,2] = 3 
    
df.iloc[:,3] = 3





    share|improve this answer



























      up vote
      2
      down vote













      you can do this



      df.iloc[:,1] = 3 #columns 2 
      
df.iloc[:,2] = 3 
      
df.iloc[:,3] = 3





      share|improve this answer

























        up vote
        2
        down vote










        up vote
        2
        down vote









        you can do this



        df.iloc[:,1] = 3 #columns 2 
        
df.iloc[:,2] = 3 
        
df.iloc[:,3] = 3





        share|improve this answer














        you can do this



        df.iloc[:,1] = 3 #columns 2 
        
df.iloc[:,2] = 3 
        
df.iloc[:,3] = 3






        share|improve this answer














        share|improve this answer



        share|improve this answer








        edited Nov 19 at 11:39









        Sociopath

        3,09961431




        3,09961431










        answered Nov 19 at 11:38









        runzhi xiao

        813




        813
























            up vote
            2
            down vote













            If you are using a loop when working with dataframes, you are almost always not on the right track.



            For this you can use a vectorized assignment:



            df[[2, 3, 4]] = 3


            Example:



            df = pd.DataFrame({1: [1, 2], 2:  [1, 2]})
            
print(df)
            
#     1  2
            
#  0  1  1
            
#  1  2  2
            

            
df[[1, 2]] = 3
            

            
print(df)
            
#     1  2
            
#  0  3  3
            
#  1  3  3





            share|improve this answer























            • Yes, this would be correct. But what if I wanted to apply this to 2000 rows of data, where the assigned values are different for each row. It now becomes more complicated, hence the loop. I created this dumbed down example, but I assume the principle would be the same.
              – embedded.95
              Nov 19 at 11:45










            • @embedded.95 Then that is an entirely different question which has been asked and answered many times before. Use pandas indexing or np.where.
              – DeepSpace
              Nov 19 at 11:49

















            up vote
            2
            down vote













            If you are using a loop when working with dataframes, you are almost always not on the right track.



            For this you can use a vectorized assignment:



            df[[2, 3, 4]] = 3


            Example:



            df = pd.DataFrame({1: [1, 2], 2:  [1, 2]})
            
print(df)
            
#     1  2
            
#  0  1  1
            
#  1  2  2
            

            
df[[1, 2]] = 3
            

            
print(df)
            
#     1  2
            
#  0  3  3
            
#  1  3  3





            share|improve this answer























            • Yes, this would be correct. But what if I wanted to apply this to 2000 rows of data, where the assigned values are different for each row. It now becomes more complicated, hence the loop. I created this dumbed down example, but I assume the principle would be the same.
              – embedded.95
              Nov 19 at 11:45










            • @embedded.95 Then that is an entirely different question which has been asked and answered many times before. Use pandas indexing or np.where.
              – DeepSpace
              Nov 19 at 11:49















            up vote
            2
            down vote










            up vote
            2
            down vote









            If you are using a loop when working with dataframes, you are almost always not on the right track.



            For this you can use a vectorized assignment:



            df[[2, 3, 4]] = 3


            Example:



            df = pd.DataFrame({1: [1, 2], 2:  [1, 2]})
            
print(df)
            
#     1  2
            
#  0  1  1
            
#  1  2  2
            

            
df[[1, 2]] = 3
            

            
print(df)
            
#     1  2
            
#  0  3  3
            
#  1  3  3





            share|improve this answer














            If you are using a loop when working with dataframes, you are almost always not on the right track.



            For this you can use a vectorized assignment:



            df[[2, 3, 4]] = 3


            Example:



            df = pd.DataFrame({1: [1, 2], 2:  [1, 2]})
            
print(df)
            
#     1  2
            
#  0  1  1
            
#  1  2  2
            

            
df[[1, 2]] = 3
            

            
print(df)
            
#     1  2
            
#  0  3  3
            
#  1  3  3






            share|improve this answer














            share|improve this answer



            share|improve this answer








            edited 2 days ago

























            answered Nov 19 at 11:38









            DeepSpace

            34.1k43765




            34.1k43765












            • Yes, this would be correct. But what if I wanted to apply this to 2000 rows of data, where the assigned values are different for each row. It now becomes more complicated, hence the loop. I created this dumbed down example, but I assume the principle would be the same.
              – embedded.95
              Nov 19 at 11:45










            • @embedded.95 Then that is an entirely different question which has been asked and answered many times before. Use pandas indexing or np.where.
              – DeepSpace
              Nov 19 at 11:49




















            • Yes, this would be correct. But what if I wanted to apply this to 2000 rows of data, where the assigned values are different for each row. It now becomes more complicated, hence the loop. I created this dumbed down example, but I assume the principle would be the same.
              – embedded.95
              Nov 19 at 11:45










            • @embedded.95 Then that is an entirely different question which has been asked and answered many times before. Use pandas indexing or np.where.
              – DeepSpace
              Nov 19 at 11:49


















            Yes, this would be correct. But what if I wanted to apply this to 2000 rows of data, where the assigned values are different for each row. It now becomes more complicated, hence the loop. I created this dumbed down example, but I assume the principle would be the same.
            – embedded.95
            Nov 19 at 11:45




            Yes, this would be correct. But what if I wanted to apply this to 2000 rows of data, where the assigned values are different for each row. It now becomes more complicated, hence the loop. I created this dumbed down example, but I assume the principle would be the same.
            – embedded.95
            Nov 19 at 11:45












            @embedded.95 Then that is an entirely different question which has been asked and answered many times before. Use pandas indexing or np.where.
            – DeepSpace
            Nov 19 at 11:49






            @embedded.95 Then that is an entirely different question which has been asked and answered many times before. Use pandas indexing or np.where.
            – DeepSpace
            Nov 19 at 11:49




















             

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