right join where key is null in python pandas with MultiIndexes

Question

I have two dataframes with MultiIndexes, and I need to right join them and then get the items on the right side which didn't have a key match.

The preferred answer for how to do right join where key is null in python pandas suggests to do something like this:

In [1]: dfa = pd.DataFrame({'A': range(5)}, index=range(5))

In [2]: dfb = pd.DataFrame({'A': range(10, 15)}, index=range(3,8))

In [3]: dfa
Out[3]: 
   A
0  0
1  1
2  2
3  3
4  4

In [4]: dfb
Out[4]: 
    A
3  10
4  11
5  12
6  13
7  14

In [5]: dfb.loc[dfb.index - dfa.index]
Out[5]: 
    A
5  12
6  13
7  14

but when I try that with my code, I get an error:

TypeError: cannot perform __sub__ with this index type: <class 'pandas.core.indexes.multi.MultiIndex'>

What options do I have given that MultiIndexes can't be subtracted from one another?

Crazy idea, can you modify your example with something that actually uses a MultiIndex? — Brad Solomon
– Brad Solomon, Commented Dec 8, 2017 at 19:18

Sachit Nagpal · Accepted Answer · 2017-12-08 19:20:13Z

1

You do not need a join to get the items that did not match. A simpler way would be using the below:

df = dfb[dfb.index.isin(dfa.index) == False]

Alternate way, that uses join, would be:

a = dfa.join(dfb, lsuffix='a', how='right')
a[a.Aa.isnull()][['A']]

I do not know your exact requirement, but to get the result, 1st is probably a neater way of achieving the goal. Regards

answered Dec 8, 2017 at 19:20

Sachit Nagpal

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