Turn Pandas Multi-Index into column

I have a dataframe with 2 index levels:

                         value
Trial    measurement
    1              0        13
                   1         3
                   2         4
    2              0       NaN
                   1        12
    3              0        34 

Which I want to turn into this:

Trial    measurement       value

    1              0        13
    1              1         3
    1              2         4
    2              0       NaN
    2              1        12
    3              0        34 

How can I best do this?

I need this because I want to aggregate the data as instructed here, but I can't select my columns like that if they are in use as indices.

Solution 1:

The reset_index() is a pandas DataFrame method that will transfer index values into the DataFrame as columns. The default setting for the parameter is drop=False (which will keep the index values as columns).

All you have to do call .reset_index() after the name of the DataFrame:

df = df.reset_index()  

Solution 2:

This doesn't really apply to your case but could be helpful for others (like myself 5 minutes ago) to know. If one's multindex have the same name like this:

                         value
Trial        Trial
    1              0        13
                   1         3
                   2         4
    2              0       NaN
                   1        12
    3              0        34 

df.reset_index(inplace=True) will fail, cause the columns that are created cannot have the same names.

So then you need to rename the multindex with df.index = df.index.set_names(['Trial', 'measurement']) to get:

                           value
Trial    measurement       

    1              0        13
    1              1         3
    1              2         4
    2              0       NaN
    2              1        12
    3              0        34 

And then df.reset_index(inplace=True) will work like a charm.

I encountered this problem after grouping by year and month on a datetime-column(not index) called live_date, which meant that both year and month were named live_date.

Solution 3:

As @cs95 mentioned in a comment, to drop only one level, use:

df.reset_index(level=[...])

This avoids having to redefine your desired index after reset.