country-converter 0.3.3

Use within python

Convert various country names to some standard names:

import country_converter as coco
some_names = ['United Rep. of Tanzania', 'Cape Verde', 'Burma', 'Iran (Islamic Republic of)', 'Korea, Republic of', "Dem. People's Rep. of Korea"]
standard_names = coco.convert(names=some_names, to='name_short')
print(standard_names)

Which results in [‘Tanzania’, ‘Cabo Verde’, ‘Myanmar’, ‘Iran’, ‘South Korea’, ‘North Korea’].

In case of multiple conversion, better performance can be achieved by instantiating a single CountryConverter object for all conversions:

import country_converter as coco
cc = coco.CountryConverter()

some_names = ['United Rep. of Tanzania', 'Cape Verde', 'Burma', 'Iran (Islamic Republic of)', 'Korea, Republic of', "Dem. People's Rep. of Korea"]

standard_names = cc.convert(names = some_names, src = 'regex', to = 'name_short')
UNmembership = cc.convert(names = some_names, src = 'regex', to = 'UNmember')
print(standard_names)
print(UNmembership)

Convert between classification schemes:

iso3_codes = ['USA', 'VUT', 'TKL', 'AUT' ]
iso2_codes = coco.convert(names = iso3_codes, src = 'ISO3', to = 'ISO2')
print(iso2_codes)

Which results in [‘US’, ‘VU’, ‘TK’, ‘AT’]

Internally the data is stored in a pandas dataframe, which can be accessed directly. For example, this can be used to filter countries for membership organisations (per year). Note: for this, an instance of CountryConverter is required.

import country_converter as coco
cc = coco.CountryConverter()

some_countries = ['Australia', 'Belgium', 'Brazil', 'Bulgaria', 'Cyprus', 'Czech Republic', 'Denmark', 'Estonia', 'Finland', 'France', 'Germany', 'Greece', 'Hungary', 'India', 'Indonesia', 'Ireland', 'Italy', 'Japan', 'Latvia', 'Lithuania', 'Luxembourg', 'Malta', 'Romania', 'Russia',  'Turkey', 'United Kingdom', 'United States']

oecd_since_1995 = cc.data[(cc.data.OECD >= 1995) & cc.data.name_short.isin(some_countries)].name_short
eu_until_1980 = cc.data[(cc.data.EU <= 1980) & cc.data.name_short.isin(some_countries)].name_short
print(oecd_since_1995)
print(eu_until_1980)

Some properties provide direct access to affiliations:

cc.EU28
cc.OECD

cc.EU27in('ISO3')

and the classification schemes available:

cc.valid_class

The regular expressions can also be used to match any list of countries to any other. For example:

match_these = ['norway', 'united_states', 'china', 'taiwan']
master_list = ['USA', 'The Swedish Kingdom', 'Norway is a Kingdom too', 'Peoples Republic of China', 'Republic of China' ]

matching_dict = coco.match(match_these, master_list)

See the IPython Notebook (country_converter_examples.ipynb) for more information.

Command line usage

The country converter package also provides a command line interface called coco.

Minimal example:

coco Cyprus Denmark Estonia 'United Kingdom'

Converts the given names to ISO3 codes based on regular expression matching. The list of names must be separated by spaces, country names consisting of multiple words must be put in quotes (‘’).

The input classification can be specified with ‘–src’ or ‘-s’, the target classification with ‘–to’ or ‘-t’.

The default output is a space separated list, this can be changed by passing a separator by ‘–output_sep’ or ‘-o’.

Thus, to convert from ISO3 to UN number codes and receive the output as comma separated list use:

coco AUT DEU VAT AUS -s ISO3 -t UNcode -o ', '

For further information call the help by

coco -h

Use in matlab

Newer (tested in 2016a) versions from Matlab allow to directly call python functions and libaries. This requires a python version >= 3.4 installed in the sytem path (e.g. through Anaconda).

To test, try this in matlab:

py.print(py.sys.version)

If this works, you can also use coco after installing it through pip (at the windows commandline - see the installing instruction above):

pip install country_converter

And in matlab:

coco = py.country_converter.CountryConverter()
countries = {'The Swedish Kingdom', 'Norway is a Kingdom too', 'Peoples Republic of China', 'Republic of China'};
ISO2_pythontype = coco.convert(countries, pyargs('to', 'ISO2'));
ISO2_cellarray = cellfun(@char,cell(ISO2_pythontype),'UniformOutput',false);

Alternativley, as a long oneliner:

short_names = cellfun(@char, cell(py.country_converter.convert({56, 276}, pyargs('src', 'UNcode', 'to', 'name_short'))), 'UniformOutput',false);

All properties of coco as explained above are also available in matlab:

coco = py.country_converter.CountryConverter();
coco.EU27
EU27ISO3 = coco.EU27in('ISO3');

These functions return a pandas DataFrame. The underlying values can be access with .values (e.g.

EU27ISO3.values

I leave it to professional matlab users to figure out how to further process them.