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compare pandas dataframes with different lengths



The 2019 Stack Overflow Developer Survey Results Are InWhere in the workflow should we deal with missing data?pandas dataframes memoryPlotting different values in pandas histogram with different colorsCreate a new column based on two columns from two different dataframesConcatenate dataframes PandasCombine Pandas DataFrames with year columnsSpearmanr on two pandas dataframesPrediction based on more dataframesPandas: How can I merge two dataframes?Joining two dataframes on the basis of specific conditions










0












$begingroup$


I have two dataframes of different lengths and I need to add a column to the first one with filtered values, e.g.



df1 = pd.DataFrame('Object':['cup', 'brick', 'board', 'stone'], 'id':[2, 8, 9, 6])
df1 = pd.DataFrame('Thing':['cup', 'board'], 'color':['blue', 'grey'])


and I want to create



df = pd.DataFrame('Thing':['cup', 'board'], 'color':['blue', 'grey'], 'id':[2, 9])


All methods I tried to use complained about different lengths.










share|improve this question











$endgroup$
















    0












    $begingroup$


    I have two dataframes of different lengths and I need to add a column to the first one with filtered values, e.g.



    df1 = pd.DataFrame('Object':['cup', 'brick', 'board', 'stone'], 'id':[2, 8, 9, 6])
    df1 = pd.DataFrame('Thing':['cup', 'board'], 'color':['blue', 'grey'])


    and I want to create



    df = pd.DataFrame('Thing':['cup', 'board'], 'color':['blue', 'grey'], 'id':[2, 9])


    All methods I tried to use complained about different lengths.










    share|improve this question











    $endgroup$














      0












      0








      0





      $begingroup$


      I have two dataframes of different lengths and I need to add a column to the first one with filtered values, e.g.



      df1 = pd.DataFrame('Object':['cup', 'brick', 'board', 'stone'], 'id':[2, 8, 9, 6])
      df1 = pd.DataFrame('Thing':['cup', 'board'], 'color':['blue', 'grey'])


      and I want to create



      df = pd.DataFrame('Thing':['cup', 'board'], 'color':['blue', 'grey'], 'id':[2, 9])


      All methods I tried to use complained about different lengths.










      share|improve this question











      $endgroup$




      I have two dataframes of different lengths and I need to add a column to the first one with filtered values, e.g.



      df1 = pd.DataFrame('Object':['cup', 'brick', 'board', 'stone'], 'id':[2, 8, 9, 6])
      df1 = pd.DataFrame('Thing':['cup', 'board'], 'color':['blue', 'grey'])


      and I want to create



      df = pd.DataFrame('Thing':['cup', 'board'], 'color':['blue', 'grey'], 'id':[2, 9])


      All methods I tried to use complained about different lengths.







      pandas






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Mar 30 at 11:06







      Grw Křemílek

















      asked Mar 30 at 8:25









      Grw KřemílekGrw Křemílek

      13




      13




















          1 Answer
          1






          active

          oldest

          votes


















          2












          $begingroup$

          You can accomplish your task by using the merge operation in pandas as follows:



          In [16]: df1 = pd.DataFrame('Object':['cup', 'brick', 'board', 'stone'], 'id':[2, 8, 9, 6])

          In [17]: df1
          Out[17]:
          Object id
          0 cup 2
          1 brick 8
          2 board 9
          3 stone 6

          In [18]: df2 = pd.DataFrame('Thing':['cup', 'board'], 'color':['blue', 'grey'])

          In [19]: df2
          Out[19]:
          Thing color
          0 cup blue
          1 board grey

          In [20]: df = df2.merge(df1, left_on='Thing',right_on='Object', how='inner')

          In [21]: df
          Out[21]:
          Thing color Object id
          0 cup blue cup 2
          1 board grey board 9


          and then drop the column (df.drop('Object', inplace=True)) that you don't need. For more details look at the official documentation here. Also, check out this to see how you can use merge and join operations in pandas to do all kinds of dataframe manipulations!






          share|improve this answer









          $endgroup$












          • $begingroup$
            that did it, thank you
            $endgroup$
            – Grw Křemílek
            Mar 30 at 11:16










          • $begingroup$
            You may select it as the answer if your problem is solved.
            $endgroup$
            – bkshi
            Mar 30 at 11:43











          Your Answer





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          1 Answer
          1






          active

          oldest

          votes








          1 Answer
          1






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          2












          $begingroup$

          You can accomplish your task by using the merge operation in pandas as follows:



          In [16]: df1 = pd.DataFrame('Object':['cup', 'brick', 'board', 'stone'], 'id':[2, 8, 9, 6])

          In [17]: df1
          Out[17]:
          Object id
          0 cup 2
          1 brick 8
          2 board 9
          3 stone 6

          In [18]: df2 = pd.DataFrame('Thing':['cup', 'board'], 'color':['blue', 'grey'])

          In [19]: df2
          Out[19]:
          Thing color
          0 cup blue
          1 board grey

          In [20]: df = df2.merge(df1, left_on='Thing',right_on='Object', how='inner')

          In [21]: df
          Out[21]:
          Thing color Object id
          0 cup blue cup 2
          1 board grey board 9


          and then drop the column (df.drop('Object', inplace=True)) that you don't need. For more details look at the official documentation here. Also, check out this to see how you can use merge and join operations in pandas to do all kinds of dataframe manipulations!






          share|improve this answer









          $endgroup$












          • $begingroup$
            that did it, thank you
            $endgroup$
            – Grw Křemílek
            Mar 30 at 11:16










          • $begingroup$
            You may select it as the answer if your problem is solved.
            $endgroup$
            – bkshi
            Mar 30 at 11:43















          2












          $begingroup$

          You can accomplish your task by using the merge operation in pandas as follows:



          In [16]: df1 = pd.DataFrame('Object':['cup', 'brick', 'board', 'stone'], 'id':[2, 8, 9, 6])

          In [17]: df1
          Out[17]:
          Object id
          0 cup 2
          1 brick 8
          2 board 9
          3 stone 6

          In [18]: df2 = pd.DataFrame('Thing':['cup', 'board'], 'color':['blue', 'grey'])

          In [19]: df2
          Out[19]:
          Thing color
          0 cup blue
          1 board grey

          In [20]: df = df2.merge(df1, left_on='Thing',right_on='Object', how='inner')

          In [21]: df
          Out[21]:
          Thing color Object id
          0 cup blue cup 2
          1 board grey board 9


          and then drop the column (df.drop('Object', inplace=True)) that you don't need. For more details look at the official documentation here. Also, check out this to see how you can use merge and join operations in pandas to do all kinds of dataframe manipulations!






          share|improve this answer









          $endgroup$












          • $begingroup$
            that did it, thank you
            $endgroup$
            – Grw Křemílek
            Mar 30 at 11:16










          • $begingroup$
            You may select it as the answer if your problem is solved.
            $endgroup$
            – bkshi
            Mar 30 at 11:43













          2












          2








          2





          $begingroup$

          You can accomplish your task by using the merge operation in pandas as follows:



          In [16]: df1 = pd.DataFrame('Object':['cup', 'brick', 'board', 'stone'], 'id':[2, 8, 9, 6])

          In [17]: df1
          Out[17]:
          Object id
          0 cup 2
          1 brick 8
          2 board 9
          3 stone 6

          In [18]: df2 = pd.DataFrame('Thing':['cup', 'board'], 'color':['blue', 'grey'])

          In [19]: df2
          Out[19]:
          Thing color
          0 cup blue
          1 board grey

          In [20]: df = df2.merge(df1, left_on='Thing',right_on='Object', how='inner')

          In [21]: df
          Out[21]:
          Thing color Object id
          0 cup blue cup 2
          1 board grey board 9


          and then drop the column (df.drop('Object', inplace=True)) that you don't need. For more details look at the official documentation here. Also, check out this to see how you can use merge and join operations in pandas to do all kinds of dataframe manipulations!






          share|improve this answer









          $endgroup$



          You can accomplish your task by using the merge operation in pandas as follows:



          In [16]: df1 = pd.DataFrame('Object':['cup', 'brick', 'board', 'stone'], 'id':[2, 8, 9, 6])

          In [17]: df1
          Out[17]:
          Object id
          0 cup 2
          1 brick 8
          2 board 9
          3 stone 6

          In [18]: df2 = pd.DataFrame('Thing':['cup', 'board'], 'color':['blue', 'grey'])

          In [19]: df2
          Out[19]:
          Thing color
          0 cup blue
          1 board grey

          In [20]: df = df2.merge(df1, left_on='Thing',right_on='Object', how='inner')

          In [21]: df
          Out[21]:
          Thing color Object id
          0 cup blue cup 2
          1 board grey board 9


          and then drop the column (df.drop('Object', inplace=True)) that you don't need. For more details look at the official documentation here. Also, check out this to see how you can use merge and join operations in pandas to do all kinds of dataframe manipulations!







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Mar 30 at 8:46









          bkshibkshi

          758212




          758212











          • $begingroup$
            that did it, thank you
            $endgroup$
            – Grw Křemílek
            Mar 30 at 11:16










          • $begingroup$
            You may select it as the answer if your problem is solved.
            $endgroup$
            – bkshi
            Mar 30 at 11:43
















          • $begingroup$
            that did it, thank you
            $endgroup$
            – Grw Křemílek
            Mar 30 at 11:16










          • $begingroup$
            You may select it as the answer if your problem is solved.
            $endgroup$
            – bkshi
            Mar 30 at 11:43















          $begingroup$
          that did it, thank you
          $endgroup$
          – Grw Křemílek
          Mar 30 at 11:16




          $begingroup$
          that did it, thank you
          $endgroup$
          – Grw Křemílek
          Mar 30 at 11:16












          $begingroup$
          You may select it as the answer if your problem is solved.
          $endgroup$
          – bkshi
          Mar 30 at 11:43




          $begingroup$
          You may select it as the answer if your problem is solved.
          $endgroup$
          – bkshi
          Mar 30 at 11:43

















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