Select rows if combination of columns are from other data frame in Python Pandas

Question

I have a large data frame with Origin, Destination and other data points. How do I efficiently select rows that have the Origin, Destination pairs of interest, i.e. I have another data frame with the pairs of interest.

df = pd.DataFrame({'Origin' : ['A','A','A','B','B','A'], 
                   'Destination' :['B','C','D','C','D','D'], 
                   'X' : ['a','b', 'c', 'd', 'e', 'f']})


I = pd.DataFrame({'Origin' : ['A','A','B'], 'Destination' : ['B','D','C']})

And I would want to return back

  Origin Destination  X
0      A           B  a
1      A           D  c
2      B           C  d
3      A           D  f

Answer 1

Use merge with default inner join and if only same joined columns in both DataFrames parameter on can be omit to:

print (pd.merge(df,I))
  Destination Origin  X
0           B      A  a
1           D      A  c
2           D      A  f
3           C      B  d

If need reorder columns:

print (pd.merge(I,df)[['Origin','Destination','X']])
  Origin Destination  X
0      A           B  a
1      A           D  c
2      A           D  f
3      B           C  d

If more columns with same names add on:

print (pd.merge(I,df, on=['Origin','Destination'])[['Origin','Destination','X']])
  Origin Destination  X
0      A           B  a
1      A           D  c
2      A           D  f
3      B           C  d

And thank you Boud for suggestion:

print (df.merge(I))
  Destination Origin  X
0           B      A  a
1           D      A  c
2           D      A  f
3           C      B  d