Merge multi-indexed with single-indexed data frames in pandas

Question

I have two dataframes. df1 is multi-indexed:

                value
first second    
a     x         0.471780
      y         0.774908
      z         0.563634
b          


        

Answer 1

As the .ix syntax is a powerful shortcut to reindexing, but in this case you are actually not doing any combined rows/column reindexing, this can be done a bit more elegantly (for my humble taste buds) with just using reindexing:

Preparation from hayden:

df1 = pd.DataFrame([['a', 'x', 0.123], ['a','x', 0.234],
                    ['a', 'y', 0.451], ['b', 'x', 0.453]],
                   columns=['first', 'second', 'value1']
                   ).set_index(['first', 'second'])
df2 = pd.DataFrame([['a', 10],['b', 20]],
                   columns=['first', 'value']).set_index(['first'])

Then this looks like this in iPython:

In [4]: df1
Out[4]: 
              value1
first second        
a     x        0.123
      x        0.234
      y        0.451
b     x        0.453

In [5]: df2
Out[5]: 
       value
first       
a         10
b         20

In [7]: df2.reindex(df1.index, level=0)
Out[7]: 
              value
first second       
a     x          10
      x          10
      y          10
b     x          20

In [8]: df1['value2'] = df2.reindex(df1.index, level=0)

In [9]: df1
Out[9]: 
              value1  value2
first second                
a     x        0.123      10
      x        0.234      10
      y        0.451      10
b     x        0.453      20

The mnemotechnic for what level you have to use in the reindex method: It states for the level that you already covered in the bigger index. So, in this case df2 already had level 0 covered of the df1.index.