Merge two DataFrames based on columns and values of a specific column with Pandas in Python 3.x

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up vote
6
down vote

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Hello i have a problem which i am not able to implement a solution on.
I have following two DataFrames:



>>> df1
A B date
1 1 01-2016
2 1 02-2017
1 2 03-2017
2 2 04-2020

>>> df2
A B 01-2016 02-2017 03-2017 04.2020
1 1 0.10 0.22 0.55 0.77
2 1 0.20 0.12 0.99 0.125
1 2 0.13 0.15 0.15 0.245
2 2 0.33 0.1 0.888 0.64


What i want is following DataFrame:



>>> df3
A B date value
1 1 01-2016 0.10
2 1 02-2017 0.12
1 2 03-2017 0.15
2 2 04-2020 0.64


I already tried following:



 summarize_dates = self.summarize_specific_column(data=df1, column='date')

 for date in summarize_dates:
 left_on = np.append(left_on, date)
 right_on = np.append(right_on, merge_columns.upper())
 result = pd.merge(left=df2, right=df1,
 left_on=left_on, right_on=right_on,
 how='right')
 print(result)


This does not work. Can you help me and suggest a more comfortable implementation? Manyy thanks in advance!






python pandas csv merge







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  • 1




    The easiest way to do this would be to melt df2 and then do a left join on df1.
    – CJ59
    3 hours ago






  • 1




    Thanks for your answer. But what do you mean with melt the Frame? Edit: Was already answered. :)
    – Michael Gann
    3 hours ago















up vote
6
down vote

favorite
1












Hello i have a problem which i am not able to implement a solution on.
I have following two DataFrames:



>>> df1
A B date
1 1 01-2016
2 1 02-2017
1 2 03-2017
2 2 04-2020

>>> df2
A B 01-2016 02-2017 03-2017 04.2020
1 1 0.10 0.22 0.55 0.77
2 1 0.20 0.12 0.99 0.125
1 2 0.13 0.15 0.15 0.245
2 2 0.33 0.1 0.888 0.64


What i want is following DataFrame:



>>> df3
A B date value
1 1 01-2016 0.10
2 1 02-2017 0.12
1 2 03-2017 0.15
2 2 04-2020 0.64


I already tried following:



 summarize_dates = self.summarize_specific_column(data=df1, column='date')

 for date in summarize_dates:
 left_on = np.append(left_on, date)
 right_on = np.append(right_on, merge_columns.upper())
 result = pd.merge(left=df2, right=df1,
 left_on=left_on, right_on=right_on,
 how='right')
 print(result)


This does not work. Can you help me and suggest a more comfortable implementation? Manyy thanks in advance!






python pandas csv merge







share|improve this question



















  • 1




    The easiest way to do this would be to melt df2 and then do a left join on df1.
    – CJ59
    3 hours ago






  • 1




    Thanks for your answer. But what do you mean with melt the Frame? Edit: Was already answered. :)
    – Michael Gann
    3 hours ago













up vote
6
down vote

favorite
1









up vote
6
down vote

favorite
1






1





Hello i have a problem which i am not able to implement a solution on.
I have following two DataFrames:



>>> df1
A B date
1 1 01-2016
2 1 02-2017
1 2 03-2017
2 2 04-2020

>>> df2
A B 01-2016 02-2017 03-2017 04.2020
1 1 0.10 0.22 0.55 0.77
2 1 0.20 0.12 0.99 0.125
1 2 0.13 0.15 0.15 0.245
2 2 0.33 0.1 0.888 0.64


What i want is following DataFrame:



>>> df3
A B date value
1 1 01-2016 0.10
2 1 02-2017 0.12
1 2 03-2017 0.15
2 2 04-2020 0.64


I already tried following:



 summarize_dates = self.summarize_specific_column(data=df1, column='date')

 for date in summarize_dates:
 left_on = np.append(left_on, date)
 right_on = np.append(right_on, merge_columns.upper())
 result = pd.merge(left=df2, right=df1,
 left_on=left_on, right_on=right_on,
 how='right')
 print(result)


This does not work. Can you help me and suggest a more comfortable implementation? Manyy thanks in advance!






python pandas csv merge







share|improve this question















Hello i have a problem which i am not able to implement a solution on.
I have following two DataFrames:



>>> df1
A B date
1 1 01-2016
2 1 02-2017
1 2 03-2017
2 2 04-2020

>>> df2
A B 01-2016 02-2017 03-2017 04.2020
1 1 0.10 0.22 0.55 0.77
2 1 0.20 0.12 0.99 0.125
1 2 0.13 0.15 0.15 0.245
2 2 0.33 0.1 0.888 0.64


What i want is following DataFrame:



>>> df3
A B date value
1 1 01-2016 0.10
2 1 02-2017 0.12
1 2 03-2017 0.15
2 2 04-2020 0.64


I already tried following:



 summarize_dates = self.summarize_specific_column(data=df1, column='date')

 for date in summarize_dates:
 left_on = np.append(left_on, date)
 right_on = np.append(right_on, merge_columns.upper())
 result = pd.merge(left=df2, right=df1,
 left_on=left_on, right_on=right_on,
 how='right')
 print(result)


This does not work. Can you help me and suggest a more comfortable implementation? Manyy thanks in advance!






python pandas csv merge




python pandas csv merge






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited 3 hours ago

























asked 3 hours ago









Michael Gann

343




343







  • 1




    The easiest way to do this would be to melt df2 and then do a left join on df1.
    – CJ59
    3 hours ago






  • 1




    Thanks for your answer. But what do you mean with melt the Frame? Edit: Was already answered. :)
    – Michael Gann
    3 hours ago













  • 1




    The easiest way to do this would be to melt df2 and then do a left join on df1.
    – CJ59
    3 hours ago






  • 1




    Thanks for your answer. But what do you mean with melt the Frame? Edit: Was already answered. :)
    – Michael Gann
    3 hours ago








1




1




The easiest way to do this would be to melt df2 and then do a left join on df1.
– CJ59
3 hours ago




The easiest way to do this would be to melt df2 and then do a left join on df1.
– CJ59
3 hours ago




1




1




Thanks for your answer. But what do you mean with melt the Frame? Edit: Was already answered. :)
– Michael Gann
3 hours ago





Thanks for your answer. But what do you mean with melt the Frame? Edit: Was already answered. :)
– Michael Gann
3 hours ago













2 Answers
2






active

oldest

votes

















up vote
9
down vote













You can melt df2 and then merge using the default 'inner' merge



df3 = df1.merge(df2.melt(id_vars = ['A', 'B'], var_name='date'))

 A B date value
0 1 1 01-2016 0.10
1 2 1 02-2017 0.12
2 1 2 03-2017 0.15
3 2 2 04-2020 0.64





share|improve this answer


















  • 1




    Thank you i will try that out!
    – Michael Gann
    3 hours ago






  • 1




    Works fine! Thank you very much!
    – Michael Gann
    2 hours ago

















up vote
3
down vote













Using lookup 



df1['value']=df2.set_index(['A','B']).lookup(df1.set_index(['A','B']).index,df1.date)
df1
Out[228]: 
 A B date value
0 1 1 01-2016 0.10
1 2 1 02-2017 0.12
2 1 2 03-2017 0.15
3 2 2 04-2020 0.64





share|improve this answer




















  • Works fine! Thank you very much!
    – Michael Gann
    2 hours ago










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2 Answers
2






active

oldest

votes








2 Answers
2






active

oldest

votes









active

oldest

votes






active

oldest

votes








up vote
9
down vote













You can melt df2 and then merge using the default 'inner' merge



df3 = df1.merge(df2.melt(id_vars = ['A', 'B'], var_name='date'))

 A B date value
0 1 1 01-2016 0.10
1 2 1 02-2017 0.12
2 1 2 03-2017 0.15
3 2 2 04-2020 0.64





share|improve this answer


















  • 1




    Thank you i will try that out!
    – Michael Gann
    3 hours ago






  • 1




    Works fine! Thank you very much!
    – Michael Gann
    2 hours ago














up vote
9
down vote













You can melt df2 and then merge using the default 'inner' merge



df3 = df1.merge(df2.melt(id_vars = ['A', 'B'], var_name='date'))

 A B date value
0 1 1 01-2016 0.10
1 2 1 02-2017 0.12
2 1 2 03-2017 0.15
3 2 2 04-2020 0.64





share|improve this answer


















  • 1




    Thank you i will try that out!
    – Michael Gann
    3 hours ago






  • 1




    Works fine! Thank you very much!
    – Michael Gann
    2 hours ago












up vote
9
down vote










up vote
9
down vote









You can melt df2 and then merge using the default 'inner' merge



df3 = df1.merge(df2.melt(id_vars = ['A', 'B'], var_name='date'))

 A B date value
0 1 1 01-2016 0.10
1 2 1 02-2017 0.12
2 1 2 03-2017 0.15
3 2 2 04-2020 0.64





share|improve this answer














You can melt df2 and then merge using the default 'inner' merge



df3 = df1.merge(df2.melt(id_vars = ['A', 'B'], var_name='date'))

 A B date value
0 1 1 01-2016 0.10
1 2 1 02-2017 0.12
2 1 2 03-2017 0.15
3 2 2 04-2020 0.64






share|improve this answer














share|improve this answer



share|improve this answer








edited 3 hours ago

























answered 3 hours ago









Vaishali

16.2k3927




16.2k3927







  • 1




    Thank you i will try that out!
    – Michael Gann
    3 hours ago






  • 1




    Works fine! Thank you very much!
    – Michael Gann
    2 hours ago












  • 1




    Thank you i will try that out!
    – Michael Gann
    3 hours ago






  • 1




    Works fine! Thank you very much!
    – Michael Gann
    2 hours ago







1




1




Thank you i will try that out!
– Michael Gann
3 hours ago




Thank you i will try that out!
– Michael Gann
3 hours ago




1




1




Works fine! Thank you very much!
– Michael Gann
2 hours ago




Works fine! Thank you very much!
– Michael Gann
2 hours ago












up vote
3
down vote













Using lookup 



df1['value']=df2.set_index(['A','B']).lookup(df1.set_index(['A','B']).index,df1.date)
df1
Out[228]: 
 A B date value
0 1 1 01-2016 0.10
1 2 1 02-2017 0.12
2 1 2 03-2017 0.15
3 2 2 04-2020 0.64





share|improve this answer




















  • Works fine! Thank you very much!
    – Michael Gann
    2 hours ago














up vote
3
down vote













Using lookup 



df1['value']=df2.set_index(['A','B']).lookup(df1.set_index(['A','B']).index,df1.date)
df1
Out[228]: 
 A B date value
0 1 1 01-2016 0.10
1 2 1 02-2017 0.12
2 1 2 03-2017 0.15
3 2 2 04-2020 0.64





share|improve this answer




















  • Works fine! Thank you very much!
    – Michael Gann
    2 hours ago












up vote
3
down vote










up vote
3
down vote









Using lookup 



df1['value']=df2.set_index(['A','B']).lookup(df1.set_index(['A','B']).index,df1.date)
df1
Out[228]: 
 A B date value
0 1 1 01-2016 0.10
1 2 1 02-2017 0.12
2 1 2 03-2017 0.15
3 2 2 04-2020 0.64





share|improve this answer












Using lookup 



df1['value']=df2.set_index(['A','B']).lookup(df1.set_index(['A','B']).index,df1.date)
df1
Out[228]: 
 A B date value
0 1 1 01-2016 0.10
1 2 1 02-2017 0.12
2 1 2 03-2017 0.15
3 2 2 04-2020 0.64






share|improve this answer












share|improve this answer



share|improve this answer










answered 3 hours ago









W-B

90.5k72754




90.5k72754











  • Works fine! Thank you very much!
    – Michael Gann
    2 hours ago
















  • Works fine! Thank you very much!
    – Michael Gann
    2 hours ago















Works fine! Thank you very much!
– Michael Gann
2 hours ago




Works fine! Thank you very much!
– Michael Gann
2 hours ago

















 

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