name	standardise-country-names
description	Standardise inconsistent country names in a dataset (e.g. "USA", "U.S.A.", "United States of America" → single canonical form). Use when a country column contains multiple spellings/aliases for the same country and the user wants them normalised.

Standardise Country Names

Normalise a country column so each country has a single canonical spelling.

When to invoke

User says the dataset has inconsistent country names, duplicates like "USA" vs "United States", mixed casing, or aliases.
Preparing data for add-iso3166 — standardising first improves lookup hit rate.

Procedure

Load the dataset and identify the country column.
Profile the column — print all unique values and their counts, sorted by frequency. Show this to the user before modifying anything.
Pick a canonical standard — ask the user which form they want:
- ISO 3166 official short name (e.g. "United States", "Russian Federation") — default.
- Common/colloquial (e.g. "United States", "Russia").
- ISO alpha-2 code ("US", "RU").
- ISO alpha-3 code ("USA", "RUS").
Build the mapping using pycountry:
- Exact name match → canonical.
- Known alias table (see add-iso3166 skill for the baseline list).
- Fuzzy match with confidence threshold (default 0.85); present low-confidence matches to the user for confirmation before applying.
Apply the mapping to a new column or overwrite the existing column — ask user which.
Report — before/after unique value counts, list of values that could not be resolved (leave original, flag in a country_standardisation_status column).
Write output with _standardised suffix.
Update the data dictionary if present.

Dependencies

pip install pandas pycountry

Edge cases

Historical names: ask whether to map to the current successor state or keep original.
Disputed territories (Taiwan, Kosovo, Western Sahara, Palestine): do not auto-resolve — ask the user.
Mixed case only (e.g. "france" vs "France"): handle via case normalisation in the mapping key, preserving the canonical output casing.

Standardise Country Names

Normalise a country column so each country has a single canonical spelling.

When to invoke

User says the dataset has inconsistent country names, duplicates like "USA" vs "United States", mixed casing, or aliases.

Preparing data for add-iso3166 — standardising first improves lookup hit rate.

Procedure

Load the dataset and identify the country column.

Profile the column — print all unique values and their counts, sorted by frequency. Show this to the user before modifying anything.

Pick a canonical standard — ask the user which form they want:

ISO 3166 official short name (e.g. "United States", "Russian Federation") — default.
Common/colloquial (e.g. "United States", "Russia").
ISO alpha-2 code ("US", "RU").
ISO alpha-3 code ("USA", "RUS").

Build the mapping using pycountry:

Exact name match → canonical.
Known alias table (see add-iso3166 skill for the baseline list).
Fuzzy match with confidence threshold (default 0.85); present low-confidence matches to the user for confirmation before applying.

Apply the mapping to a new column or overwrite the existing column — ask user which.

Report — before/after unique value counts, list of values that could not be resolved (leave original, flag in a country_standardisation_status column).

Write output with _standardised suffix.

Update the data dictionary if present.

Dependencies

pip install pandas pycountry

Edge cases

Historical names: ask whether to map to the current successor state or keep original.

Disputed territories (Taiwan, Kosovo, Western Sahara, Palestine): do not auto-resolve — ask the user.

Mixed case only (e.g. "france" vs "France"): handle via case normalisation in the mapping key, preserving the canonical output casing.

standardise-country-names