pandas: Unable to write values to single row dataframe










1














I have a single row dataframe(df) on which I want to insert value for every column using only the index numbers.
The dataframe df is in following form.



 a b c
1 0 0 0
2 0 0 0
3 0 0 0

df.iloc[[0],[1]] = predictions[:1]


This gives me the following warning and does not write anything to the row:



SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead


However when I try using



pred_row.iloc[0,1] = predictions[:1]


It gives me error



ValueError: Incompatible indexer with Series


Is there a way to write value to single row dataframe.
Predictions is any random value that I am trying to set in a particular cell of df










share|improve this question























  • What is predictions ? Can you add some sample?
    – jezrael
    Nov 12 at 6:06










  • @jezrael prediction is any random value that I am predicting and adding it to a column in df
    – apoorv parmar
    Nov 12 at 6:07










  • so what is print (type(predictions[:1])) ?
    – jezrael
    Nov 12 at 6:08










  • @jezrael <class 'pandas.core.series.Series'>
    – apoorv parmar
    Nov 12 at 6:12






  • 1




    Can you add yout complete code, what do you try? Because is not possible set one element of DataFrame by Series - array. Maybe need df.iloc[0, :] = predictions[:1].values if want set first row of df by predictions[:1]
    – jezrael
    Nov 12 at 6:15















1














I have a single row dataframe(df) on which I want to insert value for every column using only the index numbers.
The dataframe df is in following form.



 a b c
1 0 0 0
2 0 0 0
3 0 0 0

df.iloc[[0],[1]] = predictions[:1]


This gives me the following warning and does not write anything to the row:



SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead


However when I try using



pred_row.iloc[0,1] = predictions[:1]


It gives me error



ValueError: Incompatible indexer with Series


Is there a way to write value to single row dataframe.
Predictions is any random value that I am trying to set in a particular cell of df










share|improve this question























  • What is predictions ? Can you add some sample?
    – jezrael
    Nov 12 at 6:06










  • @jezrael prediction is any random value that I am predicting and adding it to a column in df
    – apoorv parmar
    Nov 12 at 6:07










  • so what is print (type(predictions[:1])) ?
    – jezrael
    Nov 12 at 6:08










  • @jezrael <class 'pandas.core.series.Series'>
    – apoorv parmar
    Nov 12 at 6:12






  • 1




    Can you add yout complete code, what do you try? Because is not possible set one element of DataFrame by Series - array. Maybe need df.iloc[0, :] = predictions[:1].values if want set first row of df by predictions[:1]
    – jezrael
    Nov 12 at 6:15













1












1








1







I have a single row dataframe(df) on which I want to insert value for every column using only the index numbers.
The dataframe df is in following form.



 a b c
1 0 0 0
2 0 0 0
3 0 0 0

df.iloc[[0],[1]] = predictions[:1]


This gives me the following warning and does not write anything to the row:



SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead


However when I try using



pred_row.iloc[0,1] = predictions[:1]


It gives me error



ValueError: Incompatible indexer with Series


Is there a way to write value to single row dataframe.
Predictions is any random value that I am trying to set in a particular cell of df










share|improve this question















I have a single row dataframe(df) on which I want to insert value for every column using only the index numbers.
The dataframe df is in following form.



 a b c
1 0 0 0
2 0 0 0
3 0 0 0

df.iloc[[0],[1]] = predictions[:1]


This gives me the following warning and does not write anything to the row:



SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead


However when I try using



pred_row.iloc[0,1] = predictions[:1]


It gives me error



ValueError: Incompatible indexer with Series


Is there a way to write value to single row dataframe.
Predictions is any random value that I am trying to set in a particular cell of df







python pandas dataframe






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 12 at 6:11

























asked Nov 12 at 6:02









apoorv parmar

317




317











  • What is predictions ? Can you add some sample?
    – jezrael
    Nov 12 at 6:06










  • @jezrael prediction is any random value that I am predicting and adding it to a column in df
    – apoorv parmar
    Nov 12 at 6:07










  • so what is print (type(predictions[:1])) ?
    – jezrael
    Nov 12 at 6:08










  • @jezrael <class 'pandas.core.series.Series'>
    – apoorv parmar
    Nov 12 at 6:12






  • 1




    Can you add yout complete code, what do you try? Because is not possible set one element of DataFrame by Series - array. Maybe need df.iloc[0, :] = predictions[:1].values if want set first row of df by predictions[:1]
    – jezrael
    Nov 12 at 6:15
















  • What is predictions ? Can you add some sample?
    – jezrael
    Nov 12 at 6:06










  • @jezrael prediction is any random value that I am predicting and adding it to a column in df
    – apoorv parmar
    Nov 12 at 6:07










  • so what is print (type(predictions[:1])) ?
    – jezrael
    Nov 12 at 6:08










  • @jezrael <class 'pandas.core.series.Series'>
    – apoorv parmar
    Nov 12 at 6:12






  • 1




    Can you add yout complete code, what do you try? Because is not possible set one element of DataFrame by Series - array. Maybe need df.iloc[0, :] = predictions[:1].values if want set first row of df by predictions[:1]
    – jezrael
    Nov 12 at 6:15















What is predictions ? Can you add some sample?
– jezrael
Nov 12 at 6:06




What is predictions ? Can you add some sample?
– jezrael
Nov 12 at 6:06












@jezrael prediction is any random value that I am predicting and adding it to a column in df
– apoorv parmar
Nov 12 at 6:07




@jezrael prediction is any random value that I am predicting and adding it to a column in df
– apoorv parmar
Nov 12 at 6:07












so what is print (type(predictions[:1])) ?
– jezrael
Nov 12 at 6:08




so what is print (type(predictions[:1])) ?
– jezrael
Nov 12 at 6:08












@jezrael <class 'pandas.core.series.Series'>
– apoorv parmar
Nov 12 at 6:12




@jezrael <class 'pandas.core.series.Series'>
– apoorv parmar
Nov 12 at 6:12




1




1




Can you add yout complete code, what do you try? Because is not possible set one element of DataFrame by Series - array. Maybe need df.iloc[0, :] = predictions[:1].values if want set first row of df by predictions[:1]
– jezrael
Nov 12 at 6:15




Can you add yout complete code, what do you try? Because is not possible set one element of DataFrame by Series - array. Maybe need df.iloc[0, :] = predictions[:1].values if want set first row of df by predictions[:1]
– jezrael
Nov 12 at 6:15












1 Answer
1






active

oldest

votes


















0














For set one element of Series to DataFrame change selecting to predictions[0]:



print (df)
a b c
1 0 0 0
2 0 0 0
3 0 0 0

predictions = pd.Series([1,2,3])
print (predictions)
0 1
1 2
2 3
dtype: int64

df.iloc[0, 1] = predictions[0]
#more general for set one element of Series by position
#df.iloc[0, 1] = predictions.iat[0]
print (df)
a b c
1 0 1 0
2 0 0 0
3 0 0 0


Details:



#scalar 
print (predictions[0])
1

#one element Series
print (predictions[:1])
0 1
dtype: int64



Also working convert one element Series to one element array, but set by scalar is simplier:



df.iloc[0, 1] = predictions[:1].values
print (df)
a b c
1 0 1 0
2 0 0 0
3 0 0 0

print (predictions[:1].values)
[1]





share|improve this answer






















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    1 Answer
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    1 Answer
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    active

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    active

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    0














    For set one element of Series to DataFrame change selecting to predictions[0]:



    print (df)
    a b c
    1 0 0 0
    2 0 0 0
    3 0 0 0

    predictions = pd.Series([1,2,3])
    print (predictions)
    0 1
    1 2
    2 3
    dtype: int64

    df.iloc[0, 1] = predictions[0]
    #more general for set one element of Series by position
    #df.iloc[0, 1] = predictions.iat[0]
    print (df)
    a b c
    1 0 1 0
    2 0 0 0
    3 0 0 0


    Details:



    #scalar 
    print (predictions[0])
    1

    #one element Series
    print (predictions[:1])
    0 1
    dtype: int64



    Also working convert one element Series to one element array, but set by scalar is simplier:



    df.iloc[0, 1] = predictions[:1].values
    print (df)
    a b c
    1 0 1 0
    2 0 0 0
    3 0 0 0

    print (predictions[:1].values)
    [1]





    share|improve this answer



























      0














      For set one element of Series to DataFrame change selecting to predictions[0]:



      print (df)
      a b c
      1 0 0 0
      2 0 0 0
      3 0 0 0

      predictions = pd.Series([1,2,3])
      print (predictions)
      0 1
      1 2
      2 3
      dtype: int64

      df.iloc[0, 1] = predictions[0]
      #more general for set one element of Series by position
      #df.iloc[0, 1] = predictions.iat[0]
      print (df)
      a b c
      1 0 1 0
      2 0 0 0
      3 0 0 0


      Details:



      #scalar 
      print (predictions[0])
      1

      #one element Series
      print (predictions[:1])
      0 1
      dtype: int64



      Also working convert one element Series to one element array, but set by scalar is simplier:



      df.iloc[0, 1] = predictions[:1].values
      print (df)
      a b c
      1 0 1 0
      2 0 0 0
      3 0 0 0

      print (predictions[:1].values)
      [1]





      share|improve this answer

























        0












        0








        0






        For set one element of Series to DataFrame change selecting to predictions[0]:



        print (df)
        a b c
        1 0 0 0
        2 0 0 0
        3 0 0 0

        predictions = pd.Series([1,2,3])
        print (predictions)
        0 1
        1 2
        2 3
        dtype: int64

        df.iloc[0, 1] = predictions[0]
        #more general for set one element of Series by position
        #df.iloc[0, 1] = predictions.iat[0]
        print (df)
        a b c
        1 0 1 0
        2 0 0 0
        3 0 0 0


        Details:



        #scalar 
        print (predictions[0])
        1

        #one element Series
        print (predictions[:1])
        0 1
        dtype: int64



        Also working convert one element Series to one element array, but set by scalar is simplier:



        df.iloc[0, 1] = predictions[:1].values
        print (df)
        a b c
        1 0 1 0
        2 0 0 0
        3 0 0 0

        print (predictions[:1].values)
        [1]





        share|improve this answer














        For set one element of Series to DataFrame change selecting to predictions[0]:



        print (df)
        a b c
        1 0 0 0
        2 0 0 0
        3 0 0 0

        predictions = pd.Series([1,2,3])
        print (predictions)
        0 1
        1 2
        2 3
        dtype: int64

        df.iloc[0, 1] = predictions[0]
        #more general for set one element of Series by position
        #df.iloc[0, 1] = predictions.iat[0]
        print (df)
        a b c
        1 0 1 0
        2 0 0 0
        3 0 0 0


        Details:



        #scalar 
        print (predictions[0])
        1

        #one element Series
        print (predictions[:1])
        0 1
        dtype: int64



        Also working convert one element Series to one element array, but set by scalar is simplier:



        df.iloc[0, 1] = predictions[:1].values
        print (df)
        a b c
        1 0 1 0
        2 0 0 0
        3 0 0 0

        print (predictions[:1].values)
        [1]






        share|improve this answer














        share|improve this answer



        share|improve this answer








        edited Nov 12 at 6:36

























        answered Nov 12 at 6:22









        jezrael

        318k22257336




        318k22257336



























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