Join 2 Pandas dataframes on specific columns accounting for nan
I have 2 pandas DataFrames dfA and dfB with X and Y columns and rows.
I need to append dfB on dfA, creating dfC. I need the appending to be on columns with column names in both dfA and dfB.
Moreover, if column name in dfA does not appear in dfB, then dfC should contain Nan values.
To simplify,
dfA looks like,
c1 c2 c3
0 AX1 AX2 AX3
1 AY1 AY2 AY3
2 AZ1 AZ2 AZ3
while dfB looks like,
c1 c2 c4
0 BX1 BX2 BX3
1 BY1 BY2 BY3
2 BZ1 BZ2 BZ3
such that dfC should look like,
c1 c2 c3
0 AX1 AX2 AX3
1 AY1 AY2 AY3
2 AZ1 AZ2 AZ3
3 BX1 BX2 Nan
4 BY1 BY2 Nan
5 BZ1 BZ2 Nan
How do I do this?
python pandas
asked Nov 23 '18 at 11:37
LucSpanLucSpan
678519
add a comment |
I have 2 pandas DataFrames dfA and dfB with X and Y columns and rows.
I need to append dfB on dfA, creating dfC. I need the appending to be on columns with column names in both dfA and dfB.
Moreover, if column name in dfA does not appear in dfB, then dfC should contain Nan values.
To simplify,
dfA looks like,
c1 c2 c3
0 AX1 AX2 AX3
1 AY1 AY2 AY3
2 AZ1 AZ2 AZ3
while dfB looks like,
c1 c2 c4
0 BX1 BX2 BX3
1 BY1 BY2 BY3
2 BZ1 BZ2 BZ3
such that dfC should look like,
c1 c2 c3
0 AX1 AX2 AX3
1 AY1 AY2 AY3
2 AZ1 AZ2 AZ3
3 BX1 BX2 Nan
4 BY1 BY2 Nan
5 BZ1 BZ2 Nan
How do I do this?
python pandas
asked Nov 23 '18 at 11:37
LucSpanLucSpan
678519
add a comment |
I have 2 pandas DataFrames dfA and dfB with X and Y columns and rows.
I need to append dfB on dfA, creating dfC. I need the appending to be on columns with column names in both dfA and dfB.
Moreover, if column name in dfA does not appear in dfB, then dfC should contain Nan values.
To simplify,
dfA looks like,
c1 c2 c3
0 AX1 AX2 AX3
1 AY1 AY2 AY3
2 AZ1 AZ2 AZ3
while dfB looks like,
c1 c2 c4
0 BX1 BX2 BX3
1 BY1 BY2 BY3
2 BZ1 BZ2 BZ3
such that dfC should look like,
c1 c2 c3
0 AX1 AX2 AX3
1 AY1 AY2 AY3
2 AZ1 AZ2 AZ3
3 BX1 BX2 Nan
4 BY1 BY2 Nan
5 BZ1 BZ2 Nan
How do I do this?
python pandas
asked Nov 23 '18 at 11:37
LucSpanLucSpan
678519
I have 2 pandas DataFrames dfA and dfB with X and Y columns and rows.
I need to append dfB on dfA, creating dfC. I need the appending to be on columns with column names in both dfA and dfB.
Moreover, if column name in dfA does not appear in dfB, then dfC should contain Nan values.
To simplify,
dfA looks like,
c1 c2 c3
0 AX1 AX2 AX3
1 AY1 AY2 AY3
2 AZ1 AZ2 AZ3
while dfB looks like,
c1 c2 c4
0 BX1 BX2 BX3
1 BY1 BY2 BY3
2 BZ1 BZ2 BZ3
such that dfC should look like,
c1 c2 c3
0 AX1 AX2 AX3
1 AY1 AY2 AY3
2 AZ1 AZ2 AZ3
3 BX1 BX2 Nan
4 BY1 BY2 Nan
5 BZ1 BZ2 Nan
How do I do this?
python pandas
python pandas
asked Nov 23 '18 at 11:37
LucSpanLucSpan
678519
asked Nov 23 '18 at 11:37
LucSpanLucSpan
678519
asked Nov 23 '18 at 11:37
LucSpanLucSpan
678519
asked Nov 23 '18 at 11:37
LucSpanLucSpan
678519
asked Nov 23 '18 at 11:37
LucSpanLucSpan
678519
678519
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
Use concat and filter columns of dfA:
df = pd.concat([dfA, dfB])[dfA.columns]
Or filter columns by intersection:
cols = dfA.columns.intersection(dfB.columns)
df = pd.concat([dfA, dfB[cols]])
answered Nov 23 '18 at 11:39
jezraeljezrael
331k24273351
add a comment |
try this,
pd.concat([dfA,dfB[['c1','c2']]])
To Avoid Hard coding columns
try this,
inter=list(set(dfA.columns)&set(dfB.columns))
print pd.concat([dfA,dfB[inter]])
answered Nov 23 '18 at 11:39
Mohamed Thasin ahMohamed Thasin ah
3,55131540
1
Thanks, but with @jezrael's answer I don't have to check column names.
– LucSpan
Nov 23 '18 at 11:43
1
@LucSpan - Solution updated
– Mohamed Thasin ah
Nov 23 '18 at 11:46
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
Use concat and filter columns of dfA:
df = pd.concat([dfA, dfB])[dfA.columns]
Or filter columns by intersection:
cols = dfA.columns.intersection(dfB.columns)
df = pd.concat([dfA, dfB[cols]])
answered Nov 23 '18 at 11:39
jezraeljezrael
331k24273351
add a comment |
Use concat and filter columns of dfA:
df = pd.concat([dfA, dfB])[dfA.columns]
Or filter columns by intersection:
cols = dfA.columns.intersection(dfB.columns)
df = pd.concat([dfA, dfB[cols]])
answered Nov 23 '18 at 11:39
jezraeljezrael
331k24273351
add a comment |
Use concat and filter columns of dfA:
df = pd.concat([dfA, dfB])[dfA.columns]
Or filter columns by intersection:
cols = dfA.columns.intersection(dfB.columns)
df = pd.concat([dfA, dfB[cols]])
answered Nov 23 '18 at 11:39
jezraeljezrael
331k24273351
Use concat and filter columns of dfA:
df = pd.concat([dfA, dfB])[dfA.columns]
Or filter columns by intersection:
cols = dfA.columns.intersection(dfB.columns)
df = pd.concat([dfA, dfB[cols]])
answered Nov 23 '18 at 11:39
jezraeljezrael
331k24273351
edited Nov 23 '18 at 11:42
answered Nov 23 '18 at 11:39
jezraeljezrael
331k24273351
answered Nov 23 '18 at 11:39
jezraeljezrael
331k24273351
answered Nov 23 '18 at 11:39
jezraeljezrael
331k24273351
331k24273351
add a comment |
add a comment |
try this,
pd.concat([dfA,dfB[['c1','c2']]])
To Avoid Hard coding columns
try this,
inter=list(set(dfA.columns)&set(dfB.columns))
print pd.concat([dfA,dfB[inter]])
answered Nov 23 '18 at 11:39
Mohamed Thasin ahMohamed Thasin ah
3,55131540
1
Thanks, but with @jezrael's answer I don't have to check column names.
– LucSpan
Nov 23 '18 at 11:43
1
@LucSpan - Solution updated
– Mohamed Thasin ah
Nov 23 '18 at 11:46
add a comment |
try this,
pd.concat([dfA,dfB[['c1','c2']]])
To Avoid Hard coding columns
try this,
inter=list(set(dfA.columns)&set(dfB.columns))
print pd.concat([dfA,dfB[inter]])
answered Nov 23 '18 at 11:39
Mohamed Thasin ahMohamed Thasin ah
3,55131540
1
Thanks, but with @jezrael's answer I don't have to check column names.
– LucSpan
Nov 23 '18 at 11:43
1
@LucSpan - Solution updated
– Mohamed Thasin ah
Nov 23 '18 at 11:46
add a comment |
try this,
pd.concat([dfA,dfB[['c1','c2']]])
To Avoid Hard coding columns
try this,
inter=list(set(dfA.columns)&set(dfB.columns))
print pd.concat([dfA,dfB[inter]])
answered Nov 23 '18 at 11:39
Mohamed Thasin ahMohamed Thasin ah
3,55131540
try this,
pd.concat([dfA,dfB[['c1','c2']]])
To Avoid Hard coding columns
try this,
inter=list(set(dfA.columns)&set(dfB.columns))
print pd.concat([dfA,dfB[inter]])
answered Nov 23 '18 at 11:39
Mohamed Thasin ahMohamed Thasin ah
3,55131540
edited Nov 23 '18 at 11:43
answered Nov 23 '18 at 11:39
Mohamed Thasin ahMohamed Thasin ah
3,55131540
answered Nov 23 '18 at 11:39
Mohamed Thasin ahMohamed Thasin ah
3,55131540
answered Nov 23 '18 at 11:39
Mohamed Thasin ahMohamed Thasin ah
3,55131540
3,55131540
1
Thanks, but with @jezrael's answer I don't have to check column names.
– LucSpan
Nov 23 '18 at 11:43
1
@LucSpan - Solution updated
– Mohamed Thasin ah
Nov 23 '18 at 11:46
add a comment |
1
Thanks, but with @jezrael's answer I don't have to check column names.
– LucSpan
Nov 23 '18 at 11:43
1
@LucSpan - Solution updated
– Mohamed Thasin ah
Nov 23 '18 at 11:46
1
1
Thanks, but with @jezrael's answer I don't have to check column names.
– LucSpan
Nov 23 '18 at 11:43
Thanks, but with @jezrael's answer I don't have to check column names.
– LucSpan
Nov 23 '18 at 11:43
1
1
@LucSpan - Solution updated
– Mohamed Thasin ah
Nov 23 '18 at 11:46
@LucSpan - Solution updated
– Mohamed Thasin ah
Nov 23 '18 at 11:46
add a comment |
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