Merge MultiIndex columns together into 1 level [duplicate]

Question

This question already has an answer here:

• Pandas - How to flatten a hierarchical index in columns 16 answers

Here's some data from another question:

date       type       value
1/1/2016   a          1
1/1/2016   b          2
1/1/2016   a          1
1/1/2016   b          4
1/2/2016   a          1
1/2/2016   b          1

Run this line of code:

x = df.groupby(['date', 'type']).value.agg(['sum', 'max']).unstack()

x should look like this:

         sum    max   
type       a  b   a  b
date                  
1/1/2016   2  6   1  4
1/2/2016   1  1   1  1

I want to combine the columns on the upper and lower level to get this:

           sum_a  sum_b   max_a  max_b
date                  
1/1/2016   2       6        1       4
1/2/2016   1       1        1       1

Is there an easy way to do this?

Answer 1

There's discussion of this here:

Python Pandas - How to flatten a hierarchical index in columns

And the consensus seems to be:

x.columns = ['_'.join(col) for col in x.columns.values]
print(x)
          sum_a  sum_b  max_a  max_b
date                                
1/1/2016      2      6      1      4
1/2/2016      1      1      1      1

Would be nice if there was an inbuilt method for this, but there doesn't seem to be.

Answer 2

Very similar solution to the above using zip:

x.columns = [x + '_' + i for x, i in zip(x.columns.get_level_values(0), x.columns.get_level_values(1))]
x
          sum_a  sum_b  max_a  max_b
date                                
1/1/2016      2      6      1      4
1/2/2016      1      1      1      1