Union of two pandas DataFrames
Say I have two data frames:
df1:
A
0 a
1 b
df2:
A
0 a
1 c
I want the result to be the union of the two frames with an extra column showing the source data frame that the row belongs to. In case of duplicates, duplicates should be removed and the respective extra column should show both sources:
A B
0 a df1, df2
1 b df1
2 c df2
I can get the concatenated data frame (df3) without duplicates as follows:
import pandas as pd
df3=pd.concat([df1,df2],ignore_index=True).drop_duplicates().reset_index(drop=True)
I can't think of/find a method to have control over what element goes where. How can I add the extra column?
Thank you very much for any tips.
python pandas dataframe merge concatenation
edited Jan 22 at 23:44
coldspeed
132k23141223
asked Jan 22 at 19:32
Leon RaiLeon Rai
607
add a comment |
Say I have two data frames:
df1:
A
0 a
1 b
df2:
A
0 a
1 c
I want the result to be the union of the two frames with an extra column showing the source data frame that the row belongs to. In case of duplicates, duplicates should be removed and the respective extra column should show both sources:
A B
0 a df1, df2
1 b df1
2 c df2
I can get the concatenated data frame (df3) without duplicates as follows:
import pandas as pd
df3=pd.concat([df1,df2],ignore_index=True).drop_duplicates().reset_index(drop=True)
I can't think of/find a method to have control over what element goes where. How can I add the extra column?
Thank you very much for any tips.
python pandas dataframe merge concatenation
edited Jan 22 at 23:44
coldspeed
132k23141223
asked Jan 22 at 19:32
Leon RaiLeon Rai
607
add a comment |
Say I have two data frames:
df1:
A
0 a
1 b
df2:
A
0 a
1 c
I want the result to be the union of the two frames with an extra column showing the source data frame that the row belongs to. In case of duplicates, duplicates should be removed and the respective extra column should show both sources:
A B
0 a df1, df2
1 b df1
2 c df2
I can get the concatenated data frame (df3) without duplicates as follows:
import pandas as pd
df3=pd.concat([df1,df2],ignore_index=True).drop_duplicates().reset_index(drop=True)
I can't think of/find a method to have control over what element goes where. How can I add the extra column?
Thank you very much for any tips.
python pandas dataframe merge concatenation
edited Jan 22 at 23:44
coldspeed
132k23141223
asked Jan 22 at 19:32
Leon RaiLeon Rai
607
Say I have two data frames:
df1:
A
0 a
1 b
df2:
A
0 a
1 c
I want the result to be the union of the two frames with an extra column showing the source data frame that the row belongs to. In case of duplicates, duplicates should be removed and the respective extra column should show both sources:
A B
0 a df1, df2
1 b df1
2 c df2
I can get the concatenated data frame (df3) without duplicates as follows:
import pandas as pd
df3=pd.concat([df1,df2],ignore_index=True).drop_duplicates().reset_index(drop=True)
I can't think of/find a method to have control over what element goes where. How can I add the extra column?
Thank you very much for any tips.
python pandas dataframe merge concatenation
python pandas dataframe merge concatenation
edited Jan 22 at 23:44
coldspeed
132k23141223
asked Jan 22 at 19:32
Leon RaiLeon Rai
607
edited Jan 22 at 23:44
coldspeed
132k23141223
asked Jan 22 at 19:32
Leon RaiLeon Rai
607
edited Jan 22 at 23:44
coldspeed
132k23141223
edited Jan 22 at 23:44
coldspeed
132k23141223
edited Jan 22 at 23:44
coldspeed
132k23141223
132k23141223
asked Jan 22 at 19:32
Leon RaiLeon Rai
607
asked Jan 22 at 19:32
Leon RaiLeon Rai
607
asked Jan 22 at 19:32
Leon RaiLeon Rai
607
607
add a comment |
add a comment |
3 Answers
3
active
oldest
votes
Merge with an indicator argument, and remap the result:
m = {'left_only': 'df1', 'right_only': 'df2', 'both': 'df1, df2'}
result = df1.merge(df2, on=['A'], how='outer', indicator='B')
result['B'] = result['B'].map(m)
result
A B
0 a df1, df2
1 b df1
2 c df2
answered Jan 22 at 19:37
coldspeedcoldspeed
132k23141223
Nice and succinct!
– cph_sto
Jan 22 at 19:53
@cph_sto Thank you! Upvoted back.
– coldspeed
Jan 22 at 19:55
1
I have learnt a lot from you.
– cph_sto
Jan 22 at 19:56
Excellent! Could you add how to do the same for intersection? outer->inner?
– Leon Rai
Jan 22 at 20:28
1
@LeonRaidf1.merge(df2, on=['A'], how='inner').assign(B='df1, df2')(since intersection implies membership in both)
– coldspeed
Jan 22 at 20:29
|
show 1 more comment
We use outer join to solve this -
df1 = pd.DataFrame({'A':['a','b']})
df2 = pd.DataFrame({'A':['a','c']})
df1['col1']='df1'
df2['col2']='df2'
df=pd.merge(df1, df2, on=['A'], how="outer").fillna('')
df['B']=df['col1']+','+df['col2']
df['B'] = df['B'].str.strip(',')
df=df[['A','B']]
df
A B
0 a df1,df2
1 b df1
2 c df2
answered Jan 22 at 19:52
cph_stocph_sto
2,3542421
thank you for the answer!
– Leon Rai
Jan 22 at 20:31
pleasure Leon :)
– cph_sto
Jan 22 at 20:38
add a comment |
Use the command below:
df3 = pd.concat([df1.assign(source='df1'), df2.assign(source='df2')])
.groupby('A')
.aggregate(list)
.reset_index()
The result will be:
A source
0 a [df1, df2]
1 b [df1]
2 c [df2]
The assign will add a column named source with value df1 and df2 to your dataframes. groupby command groups rows with same A value to single row. aggregate command describes how to aggregate other columns (source) for each group of rows with same A. I have used list aggregate function so that the source column be the list of values with same A.
answered Jan 22 at 19:46
Narges AyoubiNarges Ayoubi
564
thank you for the answer!
– Leon Rai
Jan 22 at 20:31
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
Merge with an indicator argument, and remap the result:
m = {'left_only': 'df1', 'right_only': 'df2', 'both': 'df1, df2'}
result = df1.merge(df2, on=['A'], how='outer', indicator='B')
result['B'] = result['B'].map(m)
result
A B
0 a df1, df2
1 b df1
2 c df2
answered Jan 22 at 19:37
coldspeedcoldspeed
132k23141223
Nice and succinct!
– cph_sto
Jan 22 at 19:53
@cph_sto Thank you! Upvoted back.
– coldspeed
Jan 22 at 19:55
1
I have learnt a lot from you.
– cph_sto
Jan 22 at 19:56
Excellent! Could you add how to do the same for intersection? outer->inner?
– Leon Rai
Jan 22 at 20:28
1
@LeonRaidf1.merge(df2, on=['A'], how='inner').assign(B='df1, df2')(since intersection implies membership in both)
– coldspeed
Jan 22 at 20:29
|
show 1 more comment
Merge with an indicator argument, and remap the result:
m = {'left_only': 'df1', 'right_only': 'df2', 'both': 'df1, df2'}
result = df1.merge(df2, on=['A'], how='outer', indicator='B')
result['B'] = result['B'].map(m)
result
A B
0 a df1, df2
1 b df1
2 c df2
answered Jan 22 at 19:37
coldspeedcoldspeed
132k23141223
Nice and succinct!
– cph_sto
Jan 22 at 19:53
@cph_sto Thank you! Upvoted back.
– coldspeed
Jan 22 at 19:55
1
I have learnt a lot from you.
– cph_sto
Jan 22 at 19:56
Excellent! Could you add how to do the same for intersection? outer->inner?
– Leon Rai
Jan 22 at 20:28
1
@LeonRaidf1.merge(df2, on=['A'], how='inner').assign(B='df1, df2')(since intersection implies membership in both)
– coldspeed
Jan 22 at 20:29
|
show 1 more comment
Merge with an indicator argument, and remap the result:
m = {'left_only': 'df1', 'right_only': 'df2', 'both': 'df1, df2'}
result = df1.merge(df2, on=['A'], how='outer', indicator='B')
result['B'] = result['B'].map(m)
result
A B
0 a df1, df2
1 b df1
2 c df2
answered Jan 22 at 19:37
coldspeedcoldspeed
132k23141223
Merge with an indicator argument, and remap the result:
m = {'left_only': 'df1', 'right_only': 'df2', 'both': 'df1, df2'}
result = df1.merge(df2, on=['A'], how='outer', indicator='B')
result['B'] = result['B'].map(m)
result
A B
0 a df1, df2
1 b df1
2 c df2
answered Jan 22 at 19:37
coldspeedcoldspeed
132k23141223
answered Jan 22 at 19:37
coldspeedcoldspeed
132k23141223
answered Jan 22 at 19:37
coldspeedcoldspeed
132k23141223
answered Jan 22 at 19:37
coldspeedcoldspeed
132k23141223
132k23141223
Nice and succinct!
– cph_sto
Jan 22 at 19:53
@cph_sto Thank you! Upvoted back.
– coldspeed
Jan 22 at 19:55
1
I have learnt a lot from you.
– cph_sto
Jan 22 at 19:56
Excellent! Could you add how to do the same for intersection? outer->inner?
– Leon Rai
Jan 22 at 20:28
1
@LeonRaidf1.merge(df2, on=['A'], how='inner').assign(B='df1, df2')(since intersection implies membership in both)
– coldspeed
Jan 22 at 20:29
|
show 1 more comment
Nice and succinct!
– cph_sto
Jan 22 at 19:53
@cph_sto Thank you! Upvoted back.
– coldspeed
Jan 22 at 19:55
1
I have learnt a lot from you.
– cph_sto
Jan 22 at 19:56
Excellent! Could you add how to do the same for intersection? outer->inner?
– Leon Rai
Jan 22 at 20:28
1
@LeonRaidf1.merge(df2, on=['A'], how='inner').assign(B='df1, df2')(since intersection implies membership in both)
– coldspeed
Jan 22 at 20:29
Nice and succinct!
– cph_sto
Jan 22 at 19:53
Nice and succinct!
– cph_sto
Jan 22 at 19:53
@cph_sto Thank you! Upvoted back.
– coldspeed
Jan 22 at 19:55
@cph_sto Thank you! Upvoted back.
– coldspeed
Jan 22 at 19:55
1
1
I have learnt a lot from you.
– cph_sto
Jan 22 at 19:56
I have learnt a lot from you.
– cph_sto
Jan 22 at 19:56
Excellent! Could you add how to do the same for intersection? outer->inner?
– Leon Rai
Jan 22 at 20:28
Excellent! Could you add how to do the same for intersection? outer->inner?
– Leon Rai
Jan 22 at 20:28
1
1
@LeonRai
df1.merge(df2, on=['A'], how='inner').assign(B='df1, df2') (since intersection implies membership in both)– coldspeed
Jan 22 at 20:29
@LeonRai
df1.merge(df2, on=['A'], how='inner').assign(B='df1, df2') (since intersection implies membership in both)– coldspeed
Jan 22 at 20:29
|
show 1 more comment
We use outer join to solve this -
df1 = pd.DataFrame({'A':['a','b']})
df2 = pd.DataFrame({'A':['a','c']})
df1['col1']='df1'
df2['col2']='df2'
df=pd.merge(df1, df2, on=['A'], how="outer").fillna('')
df['B']=df['col1']+','+df['col2']
df['B'] = df['B'].str.strip(',')
df=df[['A','B']]
df
A B
0 a df1,df2
1 b df1
2 c df2
answered Jan 22 at 19:52
cph_stocph_sto
2,3542421
thank you for the answer!
– Leon Rai
Jan 22 at 20:31
pleasure Leon :)
– cph_sto
Jan 22 at 20:38
add a comment |
We use outer join to solve this -
df1 = pd.DataFrame({'A':['a','b']})
df2 = pd.DataFrame({'A':['a','c']})
df1['col1']='df1'
df2['col2']='df2'
df=pd.merge(df1, df2, on=['A'], how="outer").fillna('')
df['B']=df['col1']+','+df['col2']
df['B'] = df['B'].str.strip(',')
df=df[['A','B']]
df
A B
0 a df1,df2
1 b df1
2 c df2
answered Jan 22 at 19:52
cph_stocph_sto
2,3542421
thank you for the answer!
– Leon Rai
Jan 22 at 20:31
pleasure Leon :)
– cph_sto
Jan 22 at 20:38
add a comment |
We use outer join to solve this -
df1 = pd.DataFrame({'A':['a','b']})
df2 = pd.DataFrame({'A':['a','c']})
df1['col1']='df1'
df2['col2']='df2'
df=pd.merge(df1, df2, on=['A'], how="outer").fillna('')
df['B']=df['col1']+','+df['col2']
df['B'] = df['B'].str.strip(',')
df=df[['A','B']]
df
A B
0 a df1,df2
1 b df1
2 c df2
answered Jan 22 at 19:52
cph_stocph_sto
2,3542421
We use outer join to solve this -
df1 = pd.DataFrame({'A':['a','b']})
df2 = pd.DataFrame({'A':['a','c']})
df1['col1']='df1'
df2['col2']='df2'
df=pd.merge(df1, df2, on=['A'], how="outer").fillna('')
df['B']=df['col1']+','+df['col2']
df['B'] = df['B'].str.strip(',')
df=df[['A','B']]
df
A B
0 a df1,df2
1 b df1
2 c df2
answered Jan 22 at 19:52
cph_stocph_sto
2,3542421
answered Jan 22 at 19:52
cph_stocph_sto
2,3542421
answered Jan 22 at 19:52
cph_stocph_sto
2,3542421
answered Jan 22 at 19:52
cph_stocph_sto
2,3542421
2,3542421
thank you for the answer!
– Leon Rai
Jan 22 at 20:31
pleasure Leon :)
– cph_sto
Jan 22 at 20:38
add a comment |
thank you for the answer!
– Leon Rai
Jan 22 at 20:31
pleasure Leon :)
– cph_sto
Jan 22 at 20:38
thank you for the answer!
– Leon Rai
Jan 22 at 20:31
thank you for the answer!
– Leon Rai
Jan 22 at 20:31
pleasure Leon :)
– cph_sto
Jan 22 at 20:38
pleasure Leon :)
– cph_sto
Jan 22 at 20:38
add a comment |
Use the command below:
df3 = pd.concat([df1.assign(source='df1'), df2.assign(source='df2')])
.groupby('A')
.aggregate(list)
.reset_index()
The result will be:
A source
0 a [df1, df2]
1 b [df1]
2 c [df2]
The assign will add a column named source with value df1 and df2 to your dataframes. groupby command groups rows with same A value to single row. aggregate command describes how to aggregate other columns (source) for each group of rows with same A. I have used list aggregate function so that the source column be the list of values with same A.
answered Jan 22 at 19:46
Narges AyoubiNarges Ayoubi
564
thank you for the answer!
– Leon Rai
Jan 22 at 20:31
add a comment |
Use the command below:
df3 = pd.concat([df1.assign(source='df1'), df2.assign(source='df2')])
.groupby('A')
.aggregate(list)
.reset_index()
The result will be:
A source
0 a [df1, df2]
1 b [df1]
2 c [df2]
The assign will add a column named source with value df1 and df2 to your dataframes. groupby command groups rows with same A value to single row. aggregate command describes how to aggregate other columns (source) for each group of rows with same A. I have used list aggregate function so that the source column be the list of values with same A.
answered Jan 22 at 19:46
Narges AyoubiNarges Ayoubi
564
thank you for the answer!
– Leon Rai
Jan 22 at 20:31
add a comment |
Use the command below:
df3 = pd.concat([df1.assign(source='df1'), df2.assign(source='df2')])
.groupby('A')
.aggregate(list)
.reset_index()
The result will be:
A source
0 a [df1, df2]
1 b [df1]
2 c [df2]
The assign will add a column named source with value df1 and df2 to your dataframes. groupby command groups rows with same A value to single row. aggregate command describes how to aggregate other columns (source) for each group of rows with same A. I have used list aggregate function so that the source column be the list of values with same A.
answered Jan 22 at 19:46
Narges AyoubiNarges Ayoubi
564
Use the command below:
df3 = pd.concat([df1.assign(source='df1'), df2.assign(source='df2')])
.groupby('A')
.aggregate(list)
.reset_index()
The result will be:
A source
0 a [df1, df2]
1 b [df1]
2 c [df2]
The assign will add a column named source with value df1 and df2 to your dataframes. groupby command groups rows with same A value to single row. aggregate command describes how to aggregate other columns (source) for each group of rows with same A. I have used list aggregate function so that the source column be the list of values with same A.
answered Jan 22 at 19:46
Narges AyoubiNarges Ayoubi
564
edited Jan 22 at 20:05
answered Jan 22 at 19:46
Narges AyoubiNarges Ayoubi
564
answered Jan 22 at 19:46
Narges AyoubiNarges Ayoubi
564
answered Jan 22 at 19:46
Narges AyoubiNarges Ayoubi
564
564
thank you for the answer!
– Leon Rai
Jan 22 at 20:31
add a comment |
thank you for the answer!
– Leon Rai
Jan 22 at 20:31
thank you for the answer!
– Leon Rai
Jan 22 at 20:31
thank you for the answer!
– Leon Rai
Jan 22 at 20:31
add a comment |
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