Expressing time series data in the columns rather than the rows of a DataFrame

Question

I'm struggling to articulate my problem, so I'll demonstrate via example.

Let's say I have a DataFrame that looks like this:

>>> df = pd.DataFrame([{'person': 'bob', 'year': 2016, 'production': 30, 'efficiency': .10}, {'person': 'bob', 'year': 2017, 'production': 35, 'efficiency': .11}, {'person': 'bob', 'year': 2018, 'production': 15, 'efficiency': .05}])
>>> df
   efficiency person  production  year
0        0.10    bob          30  2016
1        0.11    bob          35  2017
2        0.05    bob          15  2018

I need to produce a report that has all of the information for each person on one line. Thus, I'd like to transform the above into:

   efficiency 2016 person  production 2016  efficiency 2017  production 2017  \
0              0.1    bob               30             0.11               35

   efficiency 2018  production 2018
0             0.05               15

This code is able to make that transformation, but it is horribly inefficient:

def combine_years(df):
    final_df = None
    for name, stats in df.groupby('person'):
        agg_df = None
        for year in stats['year']:
            new_df = stats[stats.year == year].rename(columns=lambda colname: column_renamer(colname, year))
            new_df = new_df.drop('year', axis=1)
            if agg_df is None:
                agg_df = new_df
            else:
                agg_df = agg_df.merge(new_df, how='outer', on=['person'])
        if final_df is None:
            final_df = agg_df
        else:
            final_df = pd.concat([final_df, agg_df], axis=1)
    return final_df

A couple questions:

1. Is there a more general name for this type of transformation?
2. Is there a way to do this more efficiently, using functions provided by pandas?

Answer 1

set_index

I'd like the 'person' to end up in the index and leave columns as a pandas.MultiIndex

df.set_index(['person', 'year']).unstack().swaplevel(0, 1, 1).sort_index(1)

year         2016                  2017                  2018           
       efficiency production efficiency production efficiency production
person                                                                  
bob           0.1         30       0.11         35       0.05         15

---

pivot_table

df.pivot_table(index='person', columns='year').swaplevel(0, 1, 1).sort_index(1)

year         2016                  2017                  2018           
       efficiency production efficiency production efficiency production
person                                                                  
bob           0.1         30       0.11         35       0.05         15