Adding values of two Pandas series with different column names












1















I have two pandas series of the same length but with different column names. How can one add the values in them?



series.add(other, fill_value=0, axis=0) does avoid NaN-values, but the values are not added. Instead, the result is a concatenation of the two series.



Is there a way to obtain a new series consisting of the sum of the values in two series?










share|improve this question





























    1















    I have two pandas series of the same length but with different column names. How can one add the values in them?



    series.add(other, fill_value=0, axis=0) does avoid NaN-values, but the values are not added. Instead, the result is a concatenation of the two series.



    Is there a way to obtain a new series consisting of the sum of the values in two series?










    share|improve this question



























      1












      1








      1








      I have two pandas series of the same length but with different column names. How can one add the values in them?



      series.add(other, fill_value=0, axis=0) does avoid NaN-values, but the values are not added. Instead, the result is a concatenation of the two series.



      Is there a way to obtain a new series consisting of the sum of the values in two series?










      share|improve this question
















      I have two pandas series of the same length but with different column names. How can one add the values in them?



      series.add(other, fill_value=0, axis=0) does avoid NaN-values, but the values are not added. Instead, the result is a concatenation of the two series.



      Is there a way to obtain a new series consisting of the sum of the values in two series?







      python pandas indexing series






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Nov 23 '18 at 20:27









      jpp

      100k2161111




      100k2161111










      asked Nov 23 '18 at 20:12









      Sebastian AllardSebastian Allard

      84




      84
























          2 Answers
          2






          active

          oldest

          votes


















          1














          The values attribute lets you access the underlying raw numpy arrays. You can add those.



          raw_sum = series.values + other.values
          series2 = Series(raw_sum, index=series.index)


          This also works:



          series2 = series + other.values





          share|improve this answer
























          • It's worth noting that in general indices have meanings, even if they just boil down to "row numbers". If your 2 series are meant to be added but you have mismatched indices, it's worth finding out why.

            – jpp
            Nov 23 '18 at 20:28













          • Thank you! Simple solution :) The series consisted of a sport teams average home and away statistics. The columns for home statistics were named statistic_home and the corresponding away statistics were named away_statistic.

            – Sebastian Allard
            Nov 23 '18 at 20:28





















          1














          Mismatched indidces



          This issue is your 2 series have different indices. Here's an example:



          s1 = pd.Series([1, np.nan, 3, np.nan, 5], index=np.arange(5))
          s2 = pd.Series([np.nan, 7, 8, np.nan, np.nan], index=np.arange(5)+10)

          print(s1.add(s2, fill_value=0, axis=0))

          0 1.0
          1 NaN
          2 3.0
          3 NaN
          4 5.0
          10 NaN
          11 7.0
          12 8.0
          13 NaN
          14 NaN
          dtype: float64


          You have 2 options: reindex via, for example, a dictionary or disregard indices and add your series positionally.



          Map index of one series to align with the other



          You can use a dictionary to realign. The mapping below is arbitrary. NaN values occur where, after reindexing, values in both series are NaN:



          index_map = dict(zip(np.arange(5) + 10, [3, 2, 4, 0, 1]))
          s2.index = s2.index.map(index_map)

          print(s1.add(s2, fill_value=0, axis=0))

          0 1.0
          1 NaN
          2 10.0
          3 NaN
          4 13.0
          dtype: float64


          Disregard indices; use positional location only



          In this case, you can either construct a new series with the regular pd.RangeIndex as index (i.e. 0, 1, 2, ...), or use an index from one of the input series:



          # normalized index
          res = pd.Series(s1.values + s2.values)

          # take index from s1
          res = pd.Series(s1.values + s2.values, index=s1.index)





          share|improve this answer


























          • Thank you for a detailed solution :)

            – Sebastian Allard
            Nov 23 '18 at 20:35











          Your Answer






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          2 Answers
          2






          active

          oldest

          votes








          2 Answers
          2






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          1














          The values attribute lets you access the underlying raw numpy arrays. You can add those.



          raw_sum = series.values + other.values
          series2 = Series(raw_sum, index=series.index)


          This also works:



          series2 = series + other.values





          share|improve this answer
























          • It's worth noting that in general indices have meanings, even if they just boil down to "row numbers". If your 2 series are meant to be added but you have mismatched indices, it's worth finding out why.

            – jpp
            Nov 23 '18 at 20:28













          • Thank you! Simple solution :) The series consisted of a sport teams average home and away statistics. The columns for home statistics were named statistic_home and the corresponding away statistics were named away_statistic.

            – Sebastian Allard
            Nov 23 '18 at 20:28


















          1














          The values attribute lets you access the underlying raw numpy arrays. You can add those.



          raw_sum = series.values + other.values
          series2 = Series(raw_sum, index=series.index)


          This also works:



          series2 = series + other.values





          share|improve this answer
























          • It's worth noting that in general indices have meanings, even if they just boil down to "row numbers". If your 2 series are meant to be added but you have mismatched indices, it's worth finding out why.

            – jpp
            Nov 23 '18 at 20:28













          • Thank you! Simple solution :) The series consisted of a sport teams average home and away statistics. The columns for home statistics were named statistic_home and the corresponding away statistics were named away_statistic.

            – Sebastian Allard
            Nov 23 '18 at 20:28
















          1












          1








          1







          The values attribute lets you access the underlying raw numpy arrays. You can add those.



          raw_sum = series.values + other.values
          series2 = Series(raw_sum, index=series.index)


          This also works:



          series2 = series + other.values





          share|improve this answer













          The values attribute lets you access the underlying raw numpy arrays. You can add those.



          raw_sum = series.values + other.values
          series2 = Series(raw_sum, index=series.index)


          This also works:



          series2 = series + other.values






          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Nov 23 '18 at 20:18









          shx2shx2

          40.7k679110




          40.7k679110













          • It's worth noting that in general indices have meanings, even if they just boil down to "row numbers". If your 2 series are meant to be added but you have mismatched indices, it's worth finding out why.

            – jpp
            Nov 23 '18 at 20:28













          • Thank you! Simple solution :) The series consisted of a sport teams average home and away statistics. The columns for home statistics were named statistic_home and the corresponding away statistics were named away_statistic.

            – Sebastian Allard
            Nov 23 '18 at 20:28





















          • It's worth noting that in general indices have meanings, even if they just boil down to "row numbers". If your 2 series are meant to be added but you have mismatched indices, it's worth finding out why.

            – jpp
            Nov 23 '18 at 20:28













          • Thank you! Simple solution :) The series consisted of a sport teams average home and away statistics. The columns for home statistics were named statistic_home and the corresponding away statistics were named away_statistic.

            – Sebastian Allard
            Nov 23 '18 at 20:28



















          It's worth noting that in general indices have meanings, even if they just boil down to "row numbers". If your 2 series are meant to be added but you have mismatched indices, it's worth finding out why.

          – jpp
          Nov 23 '18 at 20:28







          It's worth noting that in general indices have meanings, even if they just boil down to "row numbers". If your 2 series are meant to be added but you have mismatched indices, it's worth finding out why.

          – jpp
          Nov 23 '18 at 20:28















          Thank you! Simple solution :) The series consisted of a sport teams average home and away statistics. The columns for home statistics were named statistic_home and the corresponding away statistics were named away_statistic.

          – Sebastian Allard
          Nov 23 '18 at 20:28







          Thank you! Simple solution :) The series consisted of a sport teams average home and away statistics. The columns for home statistics were named statistic_home and the corresponding away statistics were named away_statistic.

          – Sebastian Allard
          Nov 23 '18 at 20:28















          1














          Mismatched indidces



          This issue is your 2 series have different indices. Here's an example:



          s1 = pd.Series([1, np.nan, 3, np.nan, 5], index=np.arange(5))
          s2 = pd.Series([np.nan, 7, 8, np.nan, np.nan], index=np.arange(5)+10)

          print(s1.add(s2, fill_value=0, axis=0))

          0 1.0
          1 NaN
          2 3.0
          3 NaN
          4 5.0
          10 NaN
          11 7.0
          12 8.0
          13 NaN
          14 NaN
          dtype: float64


          You have 2 options: reindex via, for example, a dictionary or disregard indices and add your series positionally.



          Map index of one series to align with the other



          You can use a dictionary to realign. The mapping below is arbitrary. NaN values occur where, after reindexing, values in both series are NaN:



          index_map = dict(zip(np.arange(5) + 10, [3, 2, 4, 0, 1]))
          s2.index = s2.index.map(index_map)

          print(s1.add(s2, fill_value=0, axis=0))

          0 1.0
          1 NaN
          2 10.0
          3 NaN
          4 13.0
          dtype: float64


          Disregard indices; use positional location only



          In this case, you can either construct a new series with the regular pd.RangeIndex as index (i.e. 0, 1, 2, ...), or use an index from one of the input series:



          # normalized index
          res = pd.Series(s1.values + s2.values)

          # take index from s1
          res = pd.Series(s1.values + s2.values, index=s1.index)





          share|improve this answer


























          • Thank you for a detailed solution :)

            – Sebastian Allard
            Nov 23 '18 at 20:35
















          1














          Mismatched indidces



          This issue is your 2 series have different indices. Here's an example:



          s1 = pd.Series([1, np.nan, 3, np.nan, 5], index=np.arange(5))
          s2 = pd.Series([np.nan, 7, 8, np.nan, np.nan], index=np.arange(5)+10)

          print(s1.add(s2, fill_value=0, axis=0))

          0 1.0
          1 NaN
          2 3.0
          3 NaN
          4 5.0
          10 NaN
          11 7.0
          12 8.0
          13 NaN
          14 NaN
          dtype: float64


          You have 2 options: reindex via, for example, a dictionary or disregard indices and add your series positionally.



          Map index of one series to align with the other



          You can use a dictionary to realign. The mapping below is arbitrary. NaN values occur where, after reindexing, values in both series are NaN:



          index_map = dict(zip(np.arange(5) + 10, [3, 2, 4, 0, 1]))
          s2.index = s2.index.map(index_map)

          print(s1.add(s2, fill_value=0, axis=0))

          0 1.0
          1 NaN
          2 10.0
          3 NaN
          4 13.0
          dtype: float64


          Disregard indices; use positional location only



          In this case, you can either construct a new series with the regular pd.RangeIndex as index (i.e. 0, 1, 2, ...), or use an index from one of the input series:



          # normalized index
          res = pd.Series(s1.values + s2.values)

          # take index from s1
          res = pd.Series(s1.values + s2.values, index=s1.index)





          share|improve this answer


























          • Thank you for a detailed solution :)

            – Sebastian Allard
            Nov 23 '18 at 20:35














          1












          1








          1







          Mismatched indidces



          This issue is your 2 series have different indices. Here's an example:



          s1 = pd.Series([1, np.nan, 3, np.nan, 5], index=np.arange(5))
          s2 = pd.Series([np.nan, 7, 8, np.nan, np.nan], index=np.arange(5)+10)

          print(s1.add(s2, fill_value=0, axis=0))

          0 1.0
          1 NaN
          2 3.0
          3 NaN
          4 5.0
          10 NaN
          11 7.0
          12 8.0
          13 NaN
          14 NaN
          dtype: float64


          You have 2 options: reindex via, for example, a dictionary or disregard indices and add your series positionally.



          Map index of one series to align with the other



          You can use a dictionary to realign. The mapping below is arbitrary. NaN values occur where, after reindexing, values in both series are NaN:



          index_map = dict(zip(np.arange(5) + 10, [3, 2, 4, 0, 1]))
          s2.index = s2.index.map(index_map)

          print(s1.add(s2, fill_value=0, axis=0))

          0 1.0
          1 NaN
          2 10.0
          3 NaN
          4 13.0
          dtype: float64


          Disregard indices; use positional location only



          In this case, you can either construct a new series with the regular pd.RangeIndex as index (i.e. 0, 1, 2, ...), or use an index from one of the input series:



          # normalized index
          res = pd.Series(s1.values + s2.values)

          # take index from s1
          res = pd.Series(s1.values + s2.values, index=s1.index)





          share|improve this answer















          Mismatched indidces



          This issue is your 2 series have different indices. Here's an example:



          s1 = pd.Series([1, np.nan, 3, np.nan, 5], index=np.arange(5))
          s2 = pd.Series([np.nan, 7, 8, np.nan, np.nan], index=np.arange(5)+10)

          print(s1.add(s2, fill_value=0, axis=0))

          0 1.0
          1 NaN
          2 3.0
          3 NaN
          4 5.0
          10 NaN
          11 7.0
          12 8.0
          13 NaN
          14 NaN
          dtype: float64


          You have 2 options: reindex via, for example, a dictionary or disregard indices and add your series positionally.



          Map index of one series to align with the other



          You can use a dictionary to realign. The mapping below is arbitrary. NaN values occur where, after reindexing, values in both series are NaN:



          index_map = dict(zip(np.arange(5) + 10, [3, 2, 4, 0, 1]))
          s2.index = s2.index.map(index_map)

          print(s1.add(s2, fill_value=0, axis=0))

          0 1.0
          1 NaN
          2 10.0
          3 NaN
          4 13.0
          dtype: float64


          Disregard indices; use positional location only



          In this case, you can either construct a new series with the regular pd.RangeIndex as index (i.e. 0, 1, 2, ...), or use an index from one of the input series:



          # normalized index
          res = pd.Series(s1.values + s2.values)

          # take index from s1
          res = pd.Series(s1.values + s2.values, index=s1.index)






          share|improve this answer














          share|improve this answer



          share|improve this answer








          edited Nov 23 '18 at 20:24

























          answered Nov 23 '18 at 20:19









          jppjpp

          100k2161111




          100k2161111













          • Thank you for a detailed solution :)

            – Sebastian Allard
            Nov 23 '18 at 20:35



















          • Thank you for a detailed solution :)

            – Sebastian Allard
            Nov 23 '18 at 20:35

















          Thank you for a detailed solution :)

          – Sebastian Allard
          Nov 23 '18 at 20:35





          Thank you for a detailed solution :)

          – Sebastian Allard
          Nov 23 '18 at 20:35


















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