I want to filter a DataFrame using only 2 levels of a 3-level MultiIndex. Is there a way cant find a way to do that with .loc?
The only way I managed to do that is the following:
df=pd.DataFrame(index=pd.MultiIndex.from_tuples([(1,'a','x')
,(1,'a','y')
,(1,'b','z')
,(1,'b','x')
,(2,'c','y')
,(2,'c','z')
,(2,'a','x')
,(2,'a','y')
,(3,'b','z')
,(3,'b','x')
,(3,'c','y')
,(3,'c','z')]),
data=[20,26,43,20,65,40,87,41,84,50,5,54])
f=[(2, 'a'), (3, 'b'), (3, 'c')]
df=df.reset_index(level=2).loc[f].reset_index().set_index(['level_0','level_1','level_2'])
resulting df is:
| 0 | |||
|---|---|---|---|
| level_0 | level_1 | level_2 | |
| 2 | a | x | 87 |
| y | 41 | ||
| 3 | b | z | 84 |
| x | 50 | ||
| c | z | 5 | |
| x | 54 |
What I want is to be able to do something like df.loc[(f,slice(None))] to make the code a bit less complicated
i think f is not appropriate example becuz a and b do not overlap in 2 and 3
Let's take a from 1 and only b from 3 (becuz 1 also has b)
idx = [(1, 'a'), (3, 'b')]
df[df.index.droplevel(2).isin(idx)]
result:
0
1 a x 20
y 26
3 b z 84
x 50