How to drop 1st level index and then merge the remaining index values with custom logic for a pd DataFrame?

Question

Say I have a MultiIndex DataFrame like so:

                             price     volume
year   product   city
2010   A         LA          10        7
       B         SF          7         9 
       C         NY          7         6 
                 LA          18        21
                 SF          4         8
2011   A         LA          13        5 
       B         SF          2         4 
       C         NY          9         3
                 SF          2         0

I want to do a somewhat complex merge where the first level of the DataFrame index (year) is dropped and the duplicates in the now first level index (product) in the DataFrame get merged according to some custom logic. In this case I would like to be able to set the price column to use the value from the 2010 outer index and the volume column to use the values from the 2011 outer index, but I would like a general solution that can be applied to more columns should they exist.

Final DataFrame would look like this, where the price values are those from the 2010 index and the volume values are those from the 2011 index, where missing values are filled with NaNs.

                      price     volume
product   city
A         LA          10        5
B         SF          7         4 
C         NY          7         3 
          LA          18        NaN
          SF          4         0

Answer 1

You can select by first level by DataFrame.xs and then concat :

df = pd.concat([df.xs(2010)['price'], df.xs(2011)['volume']], axis=1)

Also is possible use loc :

df = pd.concat([df.loc[2010, 'price'], df.loc[2011, 'volume']], axis=1)

---

print (df)
              price  volume
product city               
A       LA       10     5.0
B       SF        7     4.0
C       LA       18     NaN
        NY        7     3.0
        SF        4     0.0