Compare 2 DataFrames and drop rows that do not contain corresponding ID variables

Question

I need to compare 2 DataFrames and drop rows in either that do not contain the corresponding IDs. As an example consider df1 and df2.

df1 = pd.DataFrame({'ID':[1,2,3,4],
                'Food':['Ham','Cheese','Egg','Bacon',],
                'Amount':[5,2,10,4,],
                })

df2 = pd.DataFrame({'ID':[1,2,4,5],
                'Food':['Ham','Cheese','Bacon','Chocolate Salty Balls'],
                'Amount':[6,7,15,5000],
                })

Pseudocode:

if df1['ID'] notin df2['ID']:
   df2['ID'].drop()

...and vice versa. The outcome in the example would be:

df1:

   ID    Food  Amount
0   1     Ham       5
1   2  Cheese       2
2   4   Bacon       4

df2:

   ID    Food  Amount
0   1     Ham       6
1   2  Cheese       7
2   4   Bacon      15

Answer 1

You could use a boolean mask:

msk1 = df1['ID'].isin(df2['ID'])
msk2 = df2['ID'].isin(df1['ID'])
df1 = df1[msk1]
df2 = df2[msk2]

or use set.intersection and boolean indexing:

common = set(df1['ID']).intersection(df2['ID'])
df1 = df1[df1['ID'].isin(common)]
df2 = df2[df2['ID'].isin(common)]

Output:

df1:

   ID    Food  Amount
0   1     Ham       5
1   2  Cheese       2
3   4   Bacon       4

df2:

   ID    Food  Amount
0   1     Ham       6
1   2  Cheese       7
2   4   Bacon      15