How to select second level in multiindex when using columns?

Question

I have a dataframe with this index:

index = pd.MultiIndex.from_product([['stock1','stock2'...],['price','volume'...]])

It's a useful structure for being able to do df['stock1'], but how do I select all the price data? I can't make any sense of the documentation.

I've tried the following with no luck: df[:,'price'] df[:]['price'] df.loc(axis=1)[:,'close'] df['price]

If this index style is generally agreed to be a bad idea for whatever reason, then what would be a better choice? Should I go for a multi-indexed index for the stocks as labels on the time series instead of at the column level?

EDIT - I am using the multiindex for the columns, not the index (the wording got the better of me). The examples in the documentation focus on multi-level indexes rather than column structures.

Answer 1

Also using John's data sample:

Using xs() is another way to slice a MultiIndex:

df
               0
stock1 price   1
       volume  2
stock2 price   3
       volume  4
stock3 price   5
       volume  6

df.xs('price', level=1, drop_level=False)
              0
stock1 price  1
stock2 price  3
stock3 price  5

Alternatively if you have a MultiIndex in place of columns:

df
  stock1        stock2        stock3       
   price volume  price volume  price volume
0      1      2      3      4      5      6

df.xs('price', axis=1, level=1, drop_level=False)
  stock1 stock2 stock3
   price  price  price
0      1      3      5