CODEWITHSUNDEEP
pythonpandasmulti-index
f4hy
f4hy

Reputation: 1329

Python Pandas Accessing values from second index in multi-indexed dataframe

I am not really sure how multi indexing works, so I maybe simply be trying to do the wrong things here. If I have a dataframe with

        Value
A  B         
1  1    5.67
1  2    6.87
1  3    7.23
2  1    8.67
2  2    9.87
2  3    10.23

If I want to access the elements where B=2, how would I do that? df.ix[2] gives me the A=2. To get a particular value it seems df.ix[(1,2)] but that is the purpose of the B index if you can't access it directly?

Upvotes: 11

Views: 13317

Answers (3)

Tomasz Bartkowiak
Tomasz Bartkowiak

Reputation: 14958

If you only want to return a cross-section, use xs (as mentioned by @Andy Hayden).

However, if you want to overwrite some values in the original dataframe, use pd.IndexSlice (with pd.loc) instead. Given a dataframe df:

In [73]: df
Out[73]:
                 col_1  col_2
index_1 index_2
1       1            5      6
        1            5      6
        2            5      6
2       2            5      6

if you want to overwrite with 0 all elements in col_1 where index_2 == 2 do:

In [75]: df.loc[pd.IndexSlice[:, 2], 'col_1'] = 0

In [76]: df
Out[76]:
                 col_1  col_2
index_1 index_2
1       1            5      6
        1            5      6
        2            0      6
2       2            0      6

Upvotes: 0

LMc
LMc

Reputation: 18622

Just as an alternative, you could use df.loc: 

>>> df.loc[(slice(None),2),:]
     Value
A B       
1 2   6.87
2 2   9.87

The tuple accesses the indexes in order. So, slice(None) grabs all values from index 'A', the second position limits based on the second level index, where 'B'=2 in this example. The : specifies that you want all columns, but you could subet the columns there as well.

Upvotes: 6

Andy Hayden
Andy Hayden

Reputation: 375415

You can use xs:

In [11]: df.xs(2, level='B')
Out[11]:
   Value
A
1   6.87
2   9.87

alternatively:

In [12]: df1.xs(1, level=1)
Out[12]:
   Value
A
1   5.67
2   8.67

Upvotes: 8

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