简单的熊猫MultiIndex切片

Question

I have the following DataFrame :

df_1 = DataFrame({
        "alpha" : [1,1,1,2,2,2,3,3,3] ,
        "beta" : [3,4,5,3,4,5,3,4,5] ,
        "val_1" : ["x", "y" , "z", "w", "a", "b", "v1" , "v2" , "v3" ] ,
        "val_2" : ["z1", "z2" , "z3", "w1", "w2", "w3" , "zz1" , "zz2" , "zz3" ]
    })
df_1.set_index(["alpha", "beta"], inplace=True)

I am trying to select the following highlighted rows:

That is, every row where beta is either 3 or 5.

I have gone through the pandas documentation multiple times and cannot find a way to do this. The closest I've come to what I think must be the answer is:

df_1.xs((3,5), level="beta", drop_level=False)

Which now currently fails. What is the proper indexing/slicing way to get this?

Answer 1

You can use the DF.query() method to subset based on the specified values:

df_1.query('beta == 3 or beta == 5')  # More succintly : df_1.query('beta == [3,5]')

Answer 2

Another option is to use get_level_values and isin to construct a logical series for indexing:

df_1[df_1.index.get_level_values(1).isin([3,5])]

Answer 3

You can use pd.IndexSlice . There is a very similar example directly in the documentation.

df_1.loc[pd.IndexSlice[:, [3,5]], :]


           val_1 val_2
alpha beta            
1     3        x    z1
      5        z    z3
2     3        w    w1
      5        b    w3
3     3       v1   zz1
      5       v3   zz3