countrymapper 0.5

A simple tool to normalise/standardise country names to their official country names or codes

Country Mapper

The country mapper is a Python package to map and match country names between different classifications and between different naming versions. Internally it uses regular expressions to match country names.

Motivation

It's very difficult to standardise country names when inputs differ at every turn for every country, for instance some may write US/ USA/ America, there has to be a way to map all these variations (including common spelling mistakes) to one naion, thus standarising the country names.

Addressing this challenge, the Country Mapper streamlines the process of converting between different naming conventions and versions of country names. Country Mapper operates on a database that maps various ISO and UN standards to each country, alongside its official name and a regular expression designed to capture all English versions of the country name.

Installation

countrymapper is registered at PyPI. From the command line:

pip install countrymapper --upgrade

Alternatively, the source code is available on GitHub.

The package depends on Pandas; for testing pytest is required.

Usage

Basic usage

Use within Python

Convert various country names to some standard names:

import countrymapper as coco
some_names = ["(1) Lebanon (2) Syria", "COLOMBIA; MEXICO", "Czech", "Philippine", "UK"]
standard_names = coco.convert(names=some_names, to='name_short')
print(standard_names)

Which results in [['Lebanon', 'Syria'], ['Colombia', 'Mexico'], 'Czechia', 'Philippines', 'United Kingdom'].The input format is determined automatically, based on ISO two letter, ISO three letter, ISO numeric or regular expression matching. In case of any ambiguity, the source format can be specified with the parameter 'src'.

In order to more efficiently convert Pandas Series, the pandas_convert() method can be used. The performance gain is especially significant for large Series. For a series containing 1 million rows a 4000x speedup can be achieved, compared to convert().

import countrymapper as coco
import pandas as pd
cc = coco.countrymapper()

some_countries = pd.Series(['Australia', 'Belgium', 'Brazil', 'Bulgaria', 'Cyprus', 'Czech Republic',
                  'Guatemala', 'Mexico', 'Honduras', 'Costa Rica', 'Colombia', 'Greece', 'Hungary',
                  'India', 'Indonesia', 'Ireland', 'Italy', 'Japan', 'Latvia', 'Lithuania',
                  'Luxembourg', 'Malta', 'Jamaica', 'Ireland', 'Turkey', 'United Kingdom',
                  'United States'], name='country')
 
iso3_codes = cc.pandas_convert(series=some_countries, to='ISO3')                  

Convert between classification schemes:

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

Which results in ['US', 'VU', 'TK', 'AT', 'not found']

The not found indication can be specified (e.g. not_found = 'not there'), if None is passed for 'not_found', the original entry gets passed through:

iso2_codes = coco.convert(names=iso3_codes, to='ISO2', not_found=None)
print(iso2_codes)

results in ['US', 'VU', 'TK', 'AT', 'XXX']

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 countrymapper is required.

import countrymapper as coco
cc = coco.countrymapper()

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)

All classifications can be directly accessed by:

cc.EU28
cc.OECD

cc.EU27as('ISO3')

and the classification schemes available:

cc.valid_class

There is also a method for only getting country classifications (thus omitting any grouping of countries):

cc.valid_country_classifications

If you rather need a dictionary describing the classification/membership use:

import countrymapper as coco
cc = coco.countrymapper()
cc.get_correspondence_dict('EXIO3', 'ISO3')

to also include countries not assigned within a specific classification use:

cc.get_correspondence_dict('EU27', 'ISO2', replace_nan='NonEU')

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)

Country mapper by default provides a warning to the python logging logger if no match is found. The following example demonstrates how to configure the coco logging behaviour.

import logging
import countrymapper as coco
logging.basicConfig(level=logging.INFO)
coco.convert("asdf")
# WARNING:countrymapper.countrymapper:asdf not found in regex
# Out: 'not found'

coco_logger = coco.logging.getLogger()
coco_logger.setLevel(logging.CRITICAL)
coco.convert("asdf")
# Out: 'not found'

Command line usage

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

Minimal example:

coco Cyprus DE Denmark Estonia 4 'United Kingdom' AUT

Converts the given names to ISO3 codes based on matching the input to ISO2, ISO3, ISOnumeric or 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' (or will be determined automatically), 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' (e.g -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 ', '

The command line tool also allows to specify the output for none found entries, including passing them through to the output by passing None:

coco CAN Peru US Mexico Venezuela UK Arendelle --not_found=None

and to specify an additional data file which will overwrite existing country matching

coco Congo --additional_data path/to/datafile.csv

Use in Matlab

Newer (tested in 2016a) versions of Matlab allow to directly call Python functions and libraries. This requires a Python version >= 3.4 installed in the system 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 countrymapper --upgrade

And in matlab:

coco = py.countrymapper.countrymapper()
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);

Alternatively, as a long oneliner:

short_names = cellfun(@char, cell(py.countrymapper.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.countrymapper.countrymapper();
coco.EU27
EU27ISO3 = coco.EU27as('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.

Building concordances for country aggregation

Coco provides a function for building concordance vectors, matrices and dictionaries between different classifications. This can be used in python as well as in matlab.

Classification schemes

Currently the following classification schemes are available (see also Data sources below for further information):

1. ISO2 (ISO 3166-1 alpha-2) - including UK/EL for Britain/Greece (but always convert to GB/GR)
2. ISO3 (ISO 3166-1 alpha-3)

Acknowledgements

This package was inspired by Konstantin Stadler's (https://github.com/IndEcol/country_converter).