Int64Index: 3292 entries, 0 to 3291
Data columns (total 17 columns):
"LOCATION"              3292 non-null object
Country                  3292 non-null object
INDICATOR                3292 non-null object
Indicator                3292 non-null object
MEASURE                  3292 non-null object
Measure                  3292 non-null object
INEQUALITY               3292 non-null object
Inequality               3292 non-null object
Unit Code                3292 non-null object
Unit                     3292 non-null object
PowerCode Code           3292 non-null int64
PowerCode                3292 non-null object
Reference Period Code    0 non-null float64
Reference Period         0 non-null float64
Value                    3292 non-null float64
Flag Codes               1120 non-null object
Flags                    1120 non-null object
dtypes: float64(3), int64(1), object(13)
memory usage: 462.9+ KB

>>> life_sat = pd.read_csv("oecd_bli_2015.csv", thousands=',')

>>> life_sat_total = life_sat[life_sat["INEQUALITY"]=="TOT"]

>>> life_sat_total = life_sat_total.pivot(index="Country", columns="Indicator", values="Value")

>>> life_sat_total.info()
<class 'pandas.core.frame.DataFrame'>
Index: 37 entries, Australia to United States
Data columns (total 24 columns):
Air pollution                                37 non-null float64
Assault rate                                 37 non-null float64
Consultation on rule-making                  37 non-null float64
Dwellings without basic facilities           37 non-null float64
Educational attainment                       37 non-null float64
Employees working very long hours            37 non-null float64
Employment rate                              37 non-null float64
Homicide rate                                37 non-null float64
Household net adjusted disposable income     37 non-null float64
Household net financial wealth               37 non-null float64
Housing expenditure                          37 non-null float64
Job security                                 37 non-null float64
Life expectancy                              37 non-null float64
Life satisfaction                            37 non-null float64
Long-term unemployment rate                  37 non-null float64
Personal earnings                            37 non-null float64
Quality of support network                   37 non-null float64
Rooms per person                             37 non-null float64
Self-reported health                         37 non-null float64
Student skills                               37 non-null float64
Time devoted to leisure and personal care    37 non-null float64
Voter turnout                                37 non-null float64
Water quality                                37 non-null float64
Years in education                           37 non-null float64
dtypes: float64(24)
memory usage: 7.2+ KB

Int64Index: 190 entries, 0 to 189
Data columns (total 7 columns):
Country                          190 non-null object
Subject Descriptor               189 non-null object
Units                            189 non-null object
Scale                            189 non-null object
Country/Series-specific Notes    188 non-null object
2015                             187 non-null float64
Estimates Start After            188 non-null float64
dtypes: float64(2), object(5)
memory usage: 11.9+ KB

>>> gdp_per_capita = pd.read_csv(
...     datapath+"gdp_per_capita.csv", thousands=',', delimiter='\t',
...     encoding='latin1', na_values="n/a", index_col="Country")
...
>>> gdp_per_capita.rename(columns={"2015": "GDP per capita"}, inplace=True)