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