pandas: Select dataframe columns based on another dataframe's columns

Question

I'm trying to subset a pandas dataframe based on columns in another, similar dataframe. I can do this easily in R:

df1 <- data.frame(A=1:5, B=6:10, C=11:15)
df2 <- data.frame(A=1:5, B=6:10)

#Select columns in df1 that exist in df2
df1[df1 %in% df2]
  A  B
1 1  6
2 2  7
3 3  8
4 4  9
5 5 10

#Select columns in df1 that do not exist in df2
df1[!(df1 %in% df2)]
   C
1 11
2 12
3 13
4 14
5 15

How can I do that with the pandas dataframes below?

df1 = pd.DataFrame({'A': [1,2,3,4,5],'B': [6,7,8,9,10],'C': [11,12,13,14,15]})
df2 = pd.DataFrame({'A': [1,2,3,4,5],'B': [6,7,8,9,10],})

Answer 1

In [77]: df1[df1.columns.intersection(df2.columns)]
Out[77]:
   A   B
0  1   6
1  2   7
2  3   8
3  4   9
4  5  10

In [78]: df1[df1.columns.difference(df2.columns)]
Out[78]:
    C
0  11
1  12
2  13
3  14
4  15

or similar, but not obvious:

In [92]: df1[list(set(df1) & set(df2))]
Out[92]:
    B  A
0   6  1
1   7  2
2   8  3
3   9  4
4  10  5

In [93]: df1[list(set(df1) - set(df2))]
Out[93]:
    C
0  11
1  12
2  13
3  14
4  15

Answer 2

Use isin, dropna:

df1[df1.isin(df2)].dropna(1)

   A   B
0  1   6
1  2   7
2  3   8
3  4   9
4  5  10


df1[~df1.isin(df2)].dropna(1)

    C
0  11
1  12
2  13
3  14
4  15