Access to OECD DatasourceΒΆ

Create DataStore for OECD by passing oecd at initialization.

In [1]: import pyopendata as pyod

In [2]: store = pyod.DataStore('oecd')

In [3]: store
Out[3]: OECDStore (http://stats.oecd.org/SDMX-JSON/data)

Get Trade Union Density data. The result will be a DataFrame which has DatetimeIndex as index and conutries as column. The target URL is:

We can read above URL as:

  • Resource ID: UN_DEN
In [4]: resource = store.get('UN_DEN')

In [5]: resource
Out[5]: OECDResource (http://stats.oecd.org/SDMX-JSON/data/UN_DEN/SVN+FRA+EST+SVK+BEL+ESP+LUX+ISR+SWE+GRC+CHL+DEU+DNK+AUS+IRL+AUT+ISL+KOR+FIN+NZL+USA+JPN+HUN+TUR+CHE+ITA+PRT+POL+OECD+NLD+GBR+CZE+MEX+NOR+CAN/OECD?)

In [6]: df = resource.read();

In [7]: df
Out[7]: 
Country     Australia    Austria    Belgium     Canada  Switzerland  \
Time                                                                  
1960-01-01  50.172928  67.883195  39.289583  29.162502    31.016277   
1961-01-01  49.471810  67.332974  38.320964  28.517935    30.221666   
1962-01-01  49.521062  66.657029  36.851721  27.068670    29.564944   
1963-01-01  49.163413  67.059648  37.687135  26.763945    28.907253   
1964-01-01  48.192964  66.624361  37.280876  26.438817    28.487138   
...               ...        ...        ...        ...          ...   
2009-01-01  19.312195  28.650257  51.509849  27.260124    17.337015   
2010-01-01  18.444057  28.371679  50.594410  27.408303    17.120516   
2011-01-01  18.514077  27.769651  50.374782  27.111712    16.653467   
2012-01-01  18.197189        NaN        NaN  27.462644    16.211944   
2013-01-01  17.040712        NaN        NaN  27.182000          NaN   

Country        ...       Slovak Republic   Slovenia     Sweden    Turkey  \
Time           ...                                                         
1960-01-01     ...                   NaN        NaN  72.081019       NaN   
1961-01-01     ...                   NaN        NaN  72.396761       NaN   
1962-01-01     ...                   NaN        NaN  72.932867       NaN   
1963-01-01     ...                   NaN        NaN  66.066797       NaN   
1964-01-01     ...                   NaN        NaN  66.725265       NaN   
...            ...                   ...        ...        ...       ...   
2009-01-01     ...             17.040898        NaN  68.414634  5.860599   
2010-01-01     ...             16.948282  26.272989  68.219845  5.853252   
2011-01-01     ...             16.955248  24.399640  67.496803  5.394394   
2012-01-01     ...                   NaN        NaN  67.505935  4.541725   
2013-01-01     ...                   NaN        NaN  67.732503       NaN   

Country     United States  
Time                       
1960-01-01      30.897484  
1961-01-01      29.518912  
1962-01-01      29.342769  
1963-01-01      28.513375  
1964-01-01      28.306461  
...                   ...  
2009-01-01      11.794019  
2010-01-01      11.383460  
2011-01-01      11.329488  
2012-01-01      11.078848  
2013-01-01      10.807891  

[54 rows x 35 columns]

You can access to specific data by slicing column.

In [8]: usa = df['United States']

In [9]: usa
Out[9]: 
Time
1960-01-01    30.897484
1961-01-01    29.518912
1962-01-01    29.342769
...
2011-01-01    11.329488
2012-01-01    11.078848
2013-01-01    10.807891
Name: United States, Length: 54