MultiIndex Group By in Pandas Data Frame

Question

I have a data set that contains countries and statistics on economic indicators by year, organized like so:

Country  Metric           2011   2012   2013  2014
          


        

Answer 1

Is this what you are looking for:

df = df.groupby(['Metric'])
df.get_group('GDP')

   Country Metric  2011    2012    2013    2014
0    USA     GDP     7      4       0       2
2    GB      GDP     8      7       0       7
4    FR      GDP     5      0       0       1

Answer 2

In this case, you don't actually need a groupby. You also don't have a MultiIndex. You can make one like this:

import pandas
from io import StringIO

datastring = StringIO("""\
Country  Metric           2011   2012   2013  2014
USA     GDP               7      4     0      2
USA     Pop.              2      3     0      3
GB      GDP               8      7     0      7
GB      Pop.              2      6     0      0
FR      GDP               5      0     0      1
FR      Pop.              1      1     0      5
""")
data = pandas.read_table(datastring, sep='\s\s+')
data.set_index(['Country', 'Metric'], inplace=True)

Then data looks like this:

                2011  2012  2013  2014
Country Metric                        
USA     GDP        7     4     0     2
        Pop.       2     3     0     3
GB      GDP        8     7     0     7
        Pop.       2     6     0     0
FR      GDP        5     0     0     1
        Pop.       1     1     0     5

Now to get the GDPs, you can take a cross-section of the dataframe via the xs method:

data.xs('GDP', level='Metric')

         2011  2012  2013  2014
Country                        
USA         7     4     0     2
GB          8     7     0     7
FR          5     0     0     1

It's so easy because your data are already pivoted/unstacked. IF they weren't and looked like this:

data.columns.names = ['Year']
data = data.stack()
data

Country  Metric  Year
USA      GDP     2011    7
                 2012    4
                 2013    0
                 2014    2
         Pop.    2011    2
                 2012    3
                 2013    0
                 2014    3
GB       GDP     2011    8
                 2012    7
                 2013    0
                 2014    7
         Pop.    2011    2
                 2012    6
                 2013    0
                 2014    0
FR       GDP     2011    5
                 2012    0
                 2013    0
                 2014    1
         Pop.    2011    1
                 2012    1
                 2013    0
                 2014    5

You could then use groupby to tell you something about the world as a whole:

data.groupby(level=['Metric', 'Year']).sum()
Metric  Year
GDP     2011    20
        2012    11
        2013     0
        2014    10
Pop.    2011     5
        2012    10
        2013     0
        2014     8

Or get real fancy:

data.groupby(level=['Metric', 'Year']).sum().unstack(level='Metric')
Metric  GDP  Pop.
Year             
2011     20     5
2012     11    10
2013      0     0
2014     10     8