Tools for creation and conversion from/to desired classification (the default is ISO 3166-1)

This project was inspired by pycountry and pycountry-convert modules and is a port of the Stata package pyconvertu (Stata module to convert a string variable into a classification from the default or user-provided JSON file with the help of Python 3) written in Python 3 and ADO. The tools can, for example, be used together with pandas to process pandas.DataFrame(), data, index, and / or columns (consult examples):

• convert(source_file=None, from_list=[], to_classification='') converts a tuple or a list into a classification from a built-in or user-defined JSON file using regular expressions.
• classification(source_file=None, from_classification='') returns a list created from a classification.
• info(source_file=None) prints metadata and sources from the built-in or user-defined JSON file.

Parameters:

• source_file : raw str or unicode, optional.
  Relative or absolute path to the user-defined JSON file.
• from_list : sequence of iterable.
  Input data.
• to_classification : str or unicode.
  'name_en' (English name), 'name_fr' (French name), 'iso3' (ISO 3166-1 alpha-3), 'iso2' (ISO 3166-1 alpha-2), or 'isoN' (ISO 3166-1 numeric).
• from_classification : str or unicode.
  'name_en' (English name), 'name_fr' (French name), 'iso3' (ISO 3166-1 alpha-3), 'iso2' (ISO 3166-1 alpha-2), or 'isoN' (ISO 3166-1 numeric).

source_file (if defined) replaces the default classification (ISO 3166-1). The file must contain a list of dictionaries where regex is a compulsory key in each one. The default JSON file was prepared with the help of json module:

[
    {
        "regex":    "^(.*afgh.*|\\s*AFG\\s*|\\s*AF\\s*|\\s*4\\s*)$",
        "name_en":  "Afghanistan",                  # classification A
        "name_fr":  "Afghanistan (l')",             # classification B
        "iso3":     "AFG",                          # ...
        "iso2":     "AF",
        "isoN":     "4"
    },
    ...
    {
        "metadata": {
            "name_en": "English short name",
            "name_fr": "French short name",
            "iso3": "alpha-3 code",
            "iso2": "alpha-2 code",
            "isoN": "numeric"
        }
    },
    {
        "sources": [
            "[https://www.iso.org/iso-3166-country-codes.html](ISO 3166 COUNTRY CODES)",
            "[https://en.wikipedia.org/wiki/List_of_alternative_country_names](ALTERNATIVE NAMES)"
        ]
    }
]

Returns:

• l : list.
  Processed data.

Examples:

import pandas as pd
from pyconvertu import convert
from pyconvertu import classification
from pyconvertu import info

# Create a pandas dataframe with ISO 3166-1 alpha-3 as `index'
data = [1, 2, 3, 4, 5, 6, 7]
iso3 = convert(
    from_list=['Canada', 'France', 'Germany', 'Italy', 'Japan', 'United Kingdom', 'United States'],
    to_classification='iso3'
)
pd.DataFrame(data, index=iso3)

# Create a pandas dataframe from available classifications
df = pd.DataFrame()
df['iso3'] = classification(from_classification='iso3')
for s in ['iso2', 'isoN', 'name_en', 'name_fr']:
    df[s] = convert(
        from_list=df['iso3'],
        to_classification=s
    )
print(df)

# Print information and metadata for the built-in JSON file and my_file.json
info()
info(source_file=r'my_file.json')